HIGH-RESOLUTION PHENOTYPIC PROFILING OF A EUKARYOTIC RIBOSOME

Jonathan LS Esguerra

Department of Cell and Molecular Biology, University of Gothenburg Medicinaregatan 9C, Box 462, SE-405 30, Gothenburg, Sweden

THESIS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY IN MICROBIOLOGY

With the approval of the Faculty of Science this thesis shall be publicly defended on Friday, 10th of October 2008 at 1000 HRS

Hörsal Arvid Carlsson, Academicum, Medicinaregatan 3, Gothenburg, Sweden

FACULTY OPPONENT

Univ.-Prof. Dr. Karl-Dieter Entian Excellence Center: Macromolecular Complexes

and Institute of Molecular Biosciences Johann Wolfgang Goethe University

Frankfurt, Germany

A doctoral thesis at a university in Sweden is produced either as a monograph or as collection of

papers. In the latter case, the introductory part constitutes the formal thesis, which summarizes the

accompanying papers. These have already been published or are in the form of manuscripts at various

stages (in press, submitted or accepted).

©Jonathan LS Esguerra jonathan.esguerra@gmail.com Printed by Intellecta Docusys, Gothenburg 2008 ISBN 978-91-628-7603-6

High-resolution phenotypic

profiling of a eukaryotic ribosome

JONATHAN LS ESGUERRA

Department of Cell and Molecular Biology

Gothenburg, 2008 ISBN 978-91-628-7603-6

To Åsa and Elias, who matter most.

ABSTRACT All living cells contain ribosomes, complex macromolecular assemblies of ribonucleic acid (RNA) and proteins, responsible for synthesizing polypeptides in a process called translation. High-resolution three-dimensional ribosomal structures coupled with biochemical studies have paved the way in understanding the various mechanistic events during protein synthesis. However, a systematic study dealing with the individual components and features of the ribosome is lacking. Specifically, the functional connection of each ribosomal protein and individual ribosomal RNA (rRNA) modification to cellular processes outside the domains of translation has not been fully explored.

Using the yeast Saccharomyces cerevisiae as a model eukaryotic organism, I investigated the contribution of individual ribosomal proteins and rRNA modifications to cellular fitness during growth in optimal and stress environments. I performed high-resolution phenotypic profiling on isogenic yeast strains with individual deletions in 110 cytoplasmic ribosomal protein (cRP) genes, 67 mitochondrial ribosomal protein (mRP) genes and 65 small nucleolar RNA genes (snoRNA). SnoRNAs facilitate site-specific rRNA modifications hence, their removal results to the absence of modification on the corresponding target site on the rRNA. I utilized a high-throughput phenotyping approach wherein the growth behaviors of individual deletion mutants were monitored in liquid micro-culture environmental arrays. Three physiologically-relevant growth variables, growth lag, growth rate and growth efficiency, extracted from high-resolution growth curves allowed for the precise quantification of marginal phenotypic consequences of deletions not detectable by standard, more qualitative approaches.

Detailed analysis of growth dynamics in a plethora of perturbed environments revealed a surprising range of hitherto unknown phenotypic diversity in the ribosome ranging from extreme sensitivity to extreme resistance in essentially every tested environment. Identification of dominant trends in the chemogenetic landscape allowed for the separation of the deletion mutants into distinct stress sub clusters, resolving the phenotypes into effects on different aspects of cellular physiology. Notable was the identification of phenotypes in all the sets of deletion mutants (cRP, mRP and snoRNA knockouts) exhibiting varying degrees of sensitivity to alternative carbon sources, strongly linking many of them to respiratory functions. The mix of cRP, mRP and snoRNA deletion mutants in some stress-specific sub cluster, such as the oxidative stress sub cluster, also indicated greater than expected functional overlap. The functional specialization within the yeast ribosome revealed a new level of translational complexity suggesting the existence of compositionally distinct ribosomes which are customized according to environmental cues. The direct participation of some ribosomal protein components and snoRNAs in cellular stress response was also envisioned. The implication of these hypotheses is significant not only in the basic understanding of the inner workings of the ribosome but also in providing new avenues in explaining the underlying mechanisms in the growing number of human diseases linked to defective components of the translational machinery.

PAPERS This dissertation is based on the following papers, referred to by Roman numerals in the text:

I. Esguerra J, Warringer J, Blomberg A. (2008) Functional importance of individual rRNA

2'-O-ribose methylations revealed by high-resolution phenotyping. RNA. 14:649-656.

Epub 2008 Feb 6.

II. Esguerra J, Mak R, Warringer J, Boone C, Nislow C, Blomberg A. (2008) High-resolution

phenotypic profiling of non-essential box C/D and box H/ACA snoRNA gene deletion

mutants. Manuscript.

III. Esguerra J, Warringer J, Blomberg A. (2008) Functional specialization in the eukaryotic

ribosome. Submitted.

IV. Esguerra J, Warringer J, Sunnerhagen P, Blomberg A. (2008) General translational

features of mutants lacking stress-specific classes of cytoplasmic ribosomal proteins.

Manuscript.

Other papers not included but referred to in this thesis:

V. Piccinelli P, Esguerra J, Blomberg A, Samuelsson T. (2006) Hunting for non-coding RNA

genes in yeast. In Computational identification of non-coding RNAs (PhD Thesis). Paul

Piccinelli. Institute of Biomedicine at Sahlgrenska Academy, Göteborg University.

CONTENTS

Preface .............................................................................................................................................. xiii

Aims and findings in brief ...................................................................................................... xv

Background ....................................................................................................................................... 1

1. Yeast as a model organism ................................................................................................................ 1

2. Yeast functional genomics ................................................................................................................. 3

3. Yeast ribosomes .................................................................................................................................. 8

4. Ribosome and protein synthesis..................................................................................................... 12

5. Cytoplasmic ribosomal proteins (cRPs) ......................................................................................... 13

Thesis summary ........................................................................................................................... 23

6. Diverse cellular roles of snoRNA genes ......................................................................................... 23

7. Ribosomal protein genes as “turbo” genes ................................................................................... 24

8. Paralogous cRP genes: “same, same but different” .................................................................... 24

9. Functional specialization in yeast ribosomes................................................................................ 25

10. Linking phenotypes to ribosomal structure ................................................................................ 29

Perspectives .................................................................................................................................... 31

11. SnoRNAs in cellular stress response ............................................................................................ 31

12. The “ribosome code”? ................................................................................................................... 34

13. Towards a system-wide analysis of the translational apparatus ............................................. 35

Acknowledgments ...................................................................................................................... 37

References ....................................................................................................................................... 39

ABBREVIATIONS cRP cytoplasmic ribosomal protein mRP mitochondrial ribosomal protein RNA ribonucleic acid mRNA messenger RNA rRNA ribosomal RNA snoRNA small nucleolar RNA SSU small subunit LSU large subunit RPS ribosomal protein small subunit RPL ribosomal protein large subunit r-proteins ribosomal proteins PTC peptidyl transferase center

xiii

PREFACE

Almost a decade and a half ago, a classic parable about a retired biochemist and a retired

geneticist relates the story of how the latter would tie the hands of workers in a car factory to

understand the inner workings of cars. 1 In the morning, the geneticist would randomly select

a worker and tie up his hands. Then later on the day, he would go up a hill and observe the

cars going out of the assembly line while he sips his afternoon beer. This way not only he

could figure out the function, for example of the steering wheel (when one day he saw the

cars piling up on the lawn), but could also deduce which worker was being responsible for

mounting a specific part of the car. The story of course was biased against the biochemist,

whose methods in learning about the functioning of cars, was described by the geneticist to

consist of grounding up hundreds of cars into pieces, identifying and determining the

proportion of the basic components and then remix the fractions in trying to reproduce some

aspects of the car’s functioning.

A year later after the publication of the said essay, a rebuttal obviously penned by a pissed

biochemist appeared in the same newsletter, re-telling the parable of the two retirees, but

this time from the retired biochemist’s point of view. In the new parable, the geneticist

almost died of car accident because he thought that one of the workers whose hands he tied

up was indispensible in the smooth running of the car. Of course, it turned out later that the

said worker was responsible for installing the car’s seat belt.

These parables told by people from their own camps echo the seemingly disparate

approaches and merits of genetics and biochemistry in the old days. Although the first story

started off as a lighthearted way of a genetics professor to convince his undergraduate

students about the superiority of genetic approaches over those of biochemistry, it

nevertheless highlights the misgivings in failing to recognize the importance of integrating

together knowledge from various fields to come up with a holistic understanding of life itself.

Today, we are witnessing an era of schizophrenic endeavors in the biological sciences, where

researchers in the field find it harder and harder how to categorize themselves. A trained

molecular biologist like me, performing chemo-genetic experiments and who calls the

bioinformatics room his second home (the lab bench being the first) where data analyses are

performed, is typical of the present day lab workhorses a.k.a. PhD students and post-docs.

This thesis is about hundreds of “cars”, each one lacking a unique part in one of the most

central components of the engine. The said parts ensure the car to run optimally, whether it

is on the autobahn or whether it is traversing the roughest roads. What we found, among

1 Genetics Society of America Newsletter, April 30 1993. GENErations Vol. 1, No. 3. Editor’s Note: William

Sullivan described the Triumph of Genetics over Biochemistry through a parable entitled "The Salvation of

Doug."

xiv

others, is the strengthening of previous observations suggesting that these parts are in fact

components of the turbo compartment, which is engaged most especially while road

conditions are favorable and are disengaged when conditions get tough. “Test-driving” our

cars in a plethora of road conditions, we surprisingly found patterns in their behavior

indicating that those parts may be involved in some way or another in circuitries outside the

engine ensuring the optimal running of the car in different road conditions.

Now, for fear of analogy breakdown as inevitably happens in such kind of ponderings, allow

me to shift my gear into the jargons of the field.

xv

AIMS AND FINDINGS IN BRIEF Capitalizing on the sensitivity of our phenotypic profiling methodology in detecting marginal

fitness defects, I endeavored to unravel novel cellular roles and functional links among the

components and features of yeast ribosomes by providing quantitative chemogenetic data for

a large set of deletion mutants concerning the cytoplasmic and to some extent, mitochondrial

translational machineries.

The first task was to contribute in the unmasking of the elusive function of cytoplasmic

ribosomal RNA modifications, believed to be the “fine tuners” of the protein synthesis

machinery, and take snapshots of their potential roles in a wider physiological context. To

achieve this, we ventured out in paper I with our phenotypic profiling of a limited number of

box C/D snoRNA gene deletion mutants. Site-specific nucleotide modifications in the

cytoplasmic rRNA are facilitated by snoRNA molecules. We extended our investigations in

paper II, to include both box C/D and box H/ACA snoRNA in our near-complete set of snoRNA

deletions mutants. The latter was also aimed at contributing to the Saccharomyces Deletion

Project by constructing “bar-coded” snoRNA knockout strains in the same genetic background

as the genome-wide protein-coding gene deletion collection.

In paper III, we profiled deletion knockouts of the non-essential genes encoding proteins

annotated (in the Gene Ontology project) as structural components of the cytoplasmic and

mitochondrial ribosomes. We uncovered distinct features in the chemogenetic landscape

identifying previously known functional links and potentially novel stress-specific connections

in the components of ribosomes.

Finally in paper IV, we investigated general translational features of deletion mutants lacking

stress-specific classes of yeast cytoplasmic ribosomal proteins.

All in all, our quantitative phenotypic data provided evidence for an extensive phenotypic

diversity within the eukaryotic ribosome, suggesting a high-degree of functional specialization

in various stressful environments. This functional specialization revealed a new level of

translational complexity indicating the existence of compositionally distinct ribosomes which

are customized according to environmental cues and/or implies the direct participation of

some ribosomal proteins and snoRNAs in cellular stress response.

xvi

1

BACKGROUND If I have seen further it is by standing on ye shoulders of Giants.

Sir Isaac Newton

1. YEAST AS A MODEL ORGANISM

The first ever appearance of the budding yeast, Saccharomyces cerevisiae in a scientific

setting happened when one curious Dutch fellow, by the name of Antony van Leeuwenhoek

examined a drop of fermenting beer in a thin glass tube assembled in one of his many

ingenuous single-lens contraptions (Leeuwenhoek 1680).

”I have made several observations with regard to the yeast formed by beer and have

constantly seen that it consists of globules floating in a clear substance, which I took to

be beer. I also saw quite distinctly that each globule of yeast in its turn consisted of six

distinct globules and that it was of the same size and form as the globules of our

blood…Some of them seemed to be quite round, while others were irregular, some of

them were larger than the others and seemed to consist of 2, 3 or 4 of these particles

joined together. Others, again, consisted of 6 globules forming a perfect globule of yeast.

Although I was eager to see this coagulation of globules, I have not succeeded in spite of

my efforts.” 2

But mistakenly thought as mere constituents of the grain which was used to make the wort, it

was indeed an unfortunate judgment to make in that very first microscopic description of the

budding yeast; for it would take a century and a half more, when three independent pioneers,

Cagniard-Latour, Kützing and Schwann, would ascribe alcoholic fermentation to living and

dividing yeast cells (Barnett 1998).

Nonetheless, three hundred and twenty years hence, these “minute globular particles” have

proven to be of great utility in relieving our insatiable thirst, not only for that fermented

liquid which “make glad the heart of man” (Huxley 1893), but much more in finding answers

to many of our most fundamental biological inquiries. Indeed the humble baker’s yeast had

made great strides, from being the workhorse of fermentation and food industries, to being

one of the most valuable “tools” in biological discourse.

2 Original letter in Dutch, dated 14 June 1680. Translated and annotated into English by a committee of Dutch

scientists in The Collected Letters of Antoni van Leeuwenhoek (1948). Amsterdam, Swets and Zeitlinger, Ltd.

Vol. III p. 245-251.

2

UNDERSTANDING HUMAN DISEASE GENES USING YEAST

The conservation of fundamental biochemical principles across the domains of life has made

yeast one of the most important genetic model systems. In fact there are many human

disease genes with functional homologues in yeast, specifically genes involved in heritable

mitochondrial diseases (Steinmetz, Scharfe et al. 2002; Perocchi, Mancera et al. 2008). Not

only are molecular mechanisms of these mitochondrial diseases being elucidated in yeast but

drug screening has also been performed (Schwimmer, Rak et al. 2006). Yeast has also recently

been used as a drug discovery platform in Huntington's and Parkinson's diseases (Outeiro and

Giorgini 2006).

A striking number of considered rare (<1 person in 2000, US population) diseases have been

found to be “translational” diseases, that is they involve defective components of the

translational apparatus (Scheper, van der Knaap et al. 2007). For instance, mutations in at

least three ribosomal proteins, RPS19, RPS24 and RPS17 have been found in patients with the

blood disease Diamond-Blackfan anaemia (DBA), a congenital erythroid aplasia characterized

by severe anaemia (Cmejla, Cmejlova et al. 2007; Morimoto, Lin et al. 2007; Choesmel,

Fribourg et al. 2008). Interestingly, another disease arising from defective erythroid

differentiation, the 5 q- syndrome has been ascribed to the partial loss of function of another

cytosolic ribosomal protein gene, RPS14 (Ebert, Pretz et al. 2008).

Not only defects in “gross” ribosomal components have been found to be linked with human

diseases, but also defects in the “finer” details of the ribosome such as rRNA modification. For

instance, dyskerin which is the rRNA pseudouridylating enzyme when mutated has been

linked to X-linked dyskeratosis congenita, a disease characterized by skin and bone marrow

failure (Heiss, Knight et al. 1998; Ruggero, Grisendi et al. 2003; Yoon, Peng et al. 2006).

The fact that translation is a much conserved cellular process makes the elucidation of

molecular mechanisms involved in these diseases possible in yeast. In papers I, II and III, we

profiled deletion knockouts of the structural components of the cytoplasmic and

mitochondrial ribosomes, and snoRNA knockouts, many of which have human homologs. We

uncovered a previously unknown phenotypic diversity possibly linking these ribosomal

features to new levels of translational control via changes in the ribosome or to processes

outside translation.

3

YEAST IN FUTURE RESEARCH

To date, yeast is the best studied model organism, and has always been in the forefront of

cutting edge technologies in the life sciences. Not only was yeast the first sequenced

eukaryotic genome (Goffeau, Barrell et al. 1996), it was also the first genomic test bed of

cDNA microarray technology, which allowed for the simultaneous analysis of the expression

of thousands of genes (Lashkari, DeRisi et al. 1997). Indeed yeast has been leading the way in

all the fields of “omics” of the post-genomics era, from transcriptomics and proteomics, to

metabolomics and phenomics.

Currently with the advent of systems biology, the integration of massive amount of biological

data has been of prime value. Therefore given the pioneering status of yeast in many facets of

data-generating technological breakthroughs, it is not surprising that it is again paving the

way in this exciting and very promising field (Mustacchi, Hohmann et al. 2006).

2. YEAST FUNCTIONAL GENOMICS

GENOME-WIDE STRAIN COLLECTIONS

One of the most important tools in the large scale analyses of the functions of genes and

proteins in yeast is the collection of different strains harboring either “bar-coded” gene

deletions (Winzeler, Shoemaker et al. 1999; Giaever, Chu et al. 2002) or gene constructs

aimed at further elucidation of the biochemical functions of the gene products, such as those

which can be used for complex purifications (Gavin, Bosche et al. 2002), determination of

protein levels (Ghaemmaghami, Huh et al. 2003) and protein localizations (Huh, Falvo et al.

2003). Such collections are very important resources when performing parallel bioassays as

they ensure uniform and well-defined genetic backgrounds.

Although the absence of genetic constructs involving non-coding RNA genes (ncRNA) in

collections designed for protein-coding genes is understandable, their absence in genome-

wide knockout collections is unfortunate. The yeast genome harbors close to 100 non-coding

RNA genes (not including the tRNA and rRNA genes) comprised of the spliceosomal RNA

genes, small nucleolar RNA genes, RNA components of the nuclear RNAse P and a small

number of miscellaneous ncRNA genes with novel functions such as SRG1 RNA which

regulates the transcription of its neighboring SER3 gene (Martens, Laprade et al. 2004).

4

Moreover, there are at least two ncRNA genes with unknown functions such as the RUF5

gene identified via computational methods (McCutcheon and Eddy 2003) and RNA170 gene

which is transcribed by RNA polymerase III (Olivas, Muhlrad et al. 1997). The inadvertent

exclusion of these non-coding RNA genes in genome-wide phenotypic screens, such as in

synthetic gene array (SGA) analysis (Tong, Evangelista et al. 2001; Tong, Lesage et al. 2004) or

in a high-resolution phenotyping screen in salt stress (Warringer, Ericson et al. 2003) led to

missed opportunities in unraveling their potentially novel functional networks.

In paper II, we successfully constructed 65 snoRNA gene deletion strains in the genetic

background of the BY-strain series to complement the existing protein-coding gene deletion

collection of the Saccharomyces Genome Deletion Project (Winzeler, Shoemaker et al. 1999).

All mutants have also been labeled with sequence barcodes to enable large-scale microarray-

based competition assays (Pierce, Davis et al. 2007; Yan, Costanzo et al. 2008). This will be an

invaluable resource towards a more complete functional characterization of the yeast

genome.

YEAST PHENOMICS

The genomic era saw the exponential accumulation of biological sequence data while

functional characterization of the actual corresponding genes and their products lagged

behind. The post-genomic era therefore saw the need to develop large scale bioassay

methodologies, which in turn spawned the birth of various “omics”, aimed at elucidating the

functional and regulatory networks of gene products en masse.

Phenomics compared with the other “omics” has an entirely different operational definition.

Whereas transcriptomics deals with transcripts, proteomics deals with proteins and

metabolomics deals with metabolic products, phenomics has no defined set of bio-molecules

in which it is focused upon. Instead, it deals with identifying and integrating the diverse

characteristics of the whole organism (phenotypes) observed under some administered

genetic and/or environmental perturbations. The ultimate aim of phenomics is therefore the

mapping of the complex relationship between genotype and phenotype of an organism in a

defined environment.

A phenomics screen involves the scoring of phenotypic consequences of gene deletions or

mutations. In yeast, a classical way to monitor phenotypes is to visually compare the growth

5

characteristics of the mutant versus the wild type in solid media. Such qualitative and rather

subjective approach is straightforward and works very well, provided that the growth defects

are clearly distinguishable. Indeed this is still the most widely used technique in yeast genetics

when investigating a limited number of mutant strains. The so called drop test, where yeast

populations are serially-diluted and plated on solid media is quite effective in demonstrating

the effects of mutation with respect to the applied environmental perturbation. This

approach has in fact been used as a final scoring method to identify 100 novel genes affecting

yeast resistance to an immunosuppressive drug in a phenotypic screen involving 4787

knockout strains derived from the BY4742 genetic background (Desmoucelles, Pinson et al.

2002).

Robotics enabled phenotypic screening on solid media to be more expeditious by “pinning”

cells on high-density ordered arrays of hundreds of colonies in a single plate. In one of the

very first applications of the technique, close to 5000 viable gene deletion mutants were

screened for sensitivity towards the DNA-damaging agent methyl methanesulfonate,

identifying 103 genes required for growth in the said agent (Chang, Bellaoui et al. 2002).

Despite the success of solid media-based phenotypic scoring in genome-wide phenotypic

screens, the inherent subjectivity in judging colony sizes poses a problem for mutations

conferring very subtle phenotypic effects. Indeed, many genes when deleted failed to

produce easily identifiable phenotypes on plates (Oliver, van der Aart et al. 1992). Thus the

main limitation of qualitative phenotypic assays on solid media plates is its lack of sensitivity

to detect very small fitness benefit of apparently non-essential genes to cellular growth. Such

genes are widespread in the yeast genome and the finding that many genes may never be

essential for survival regardless of environmental condition but instead make small fitness

contributions, led to the formulation of the “marginal benefit” hypothesis (Thatcher, Shaw et

al. 1998).

To unravel marginal fitness phenotypes, quantitative approaches had to be devised. The “bar

coding” technique, wherein knockouts were tagged with two unique 20-mer sequences

serving as strain identifiers, allowed for a large number of deletion strains to be pooled

together and their growth characteristics analyzed in parallel (Winzeler, Shoemaker et al.

1999). Essentially, the relative abundances of each strain in such competition experiment

were measured by first amplifying the tags using fluorescently-labeled universal primers and

then subsequently hybridizing them to high-density arrays containing the complementary

6

sequences. Relative growth rates for each strain could then be derived from the signals in the

array data, providing quantitative fitness measure for each deletant. In the first screen of the

near-complete set of yeast knockout mutants, the bar coding approach was able to uncover

many previously unidentified phenotypes both in optimal growth conditions and in other six

well-studied stress environments (Giaever, Chu et al. 2002).

In papers I-IV, the yeast phenotypic profiling approach employed, involved parallel micro-

cultures of isogenic single gene knock-out mutants screened in a wide variety of

environmental insults affecting a wide-range of cellular targets. This liquid-based culturing

permitted high-resolution monitoring of the behavior of the growing cell population via

optical measurements every twenty minutes. Relevant quantitative physiological parameters

namely, lag time, rate and efficiency of growth (cell density reached) were extracted from the

growth curves (Fig 1) (Warringer and Blomberg 2003; Warringer, Anevski et al. 2008). The

preciseness and utility of this phenomics methodology has been established in previous

studies (Warringer, Ericson et al. 2003; Warringer, Ericson et al. 2005; Ericson 2006;

Osterberg, Kim et al. 2006; Warringer, Anevski et al. 2008), demonstrating its ability to

uncover already known, and more importantly, marginal phenotypes with regards to specific

cellular pathways and stress protection systems in the yeast cell.

Figure 1. Phenotypic profiling via extraction of quantitative growth variables from high-resolution growth curves. A) Wild type yeast grown in different environments. Curve A

represents no stress growth, while the other curves represent growth behavior affecting different aspects of growth dynamics. Classical phenotyping only considers composite growth at any time, T, which may not encapsulate specific features of cell physiology. B) The definition of the three quantitative variables derived from the high-resolution growth curve. Figure reprinted from (Warringer, Anevski et al. 2008) under the BMC Open Access License Agreement and with the authors’ permission.

7

Yeast phenomics just like the other “omics”, is not only about the methodology itself which

yields vast amount of data. These “omics” also entail development of tools to analyze the

data being generated from a given bioassay. Results of large-scale phenomics require some

degree of algorithmic sophistication implemented in a computer. Indeed, all our phenomics

data are stored, processed, and maintained in a specialized online database

(http://cmb.gu.se/Prophecy/), aptly named PROPHECY for PROfiling of PHEnotypic

Characteristics in Yeast (Fernandez-Ricaud, Warringer et al. 2007).

POST-GENOMICS BIOINFORMATICS

One of the main challenges of the genomic era was the identification of informative

sequences, such as identification of genes and regulatory elements, from the sequenced

genomes. The combination of existing knowledge about gene structures, statistical sequence

analysis and probability models e.g. Hidden markovs was the main staple of bioinformatics

approaches in analyzing astronomical amount of sequence data. Indeed, a successful

implementation of such computational approach allowed for the complete identification of

all snoRNAs in the yeast genome (Lowe and Eddy 1999; Schattner, Decatur et al. 2004). In this

case, pre-existing biological information about the snoRNA classes, such as their characteristic

secondary structures and sequence motifs have been of particular value to the search

algorithm. However, for novel non-coding RNA gene classes or regulatory elements, their de

novo identification required more powerful approaches.

Advances in DNA sequencing technologies contributed tremendously in genome-wide

sequencing efforts not only of various species of organisms from distant niches of

evolutionary tree to closely-related species and even within species. This gave birth to

comparative genomics which unleashed new predictive power in the identification of new

genes and regulatory elements in genomes.

In paper V, we employed comparative genomics-based computational approaches to predict

novel non-coding RNA (ncRNA) gene families in yeast. As opposed to the methods to predict

guide snoRNA, which were restricted and customized to such RNA classes, we used two de

novo methods implemented in programs called QRNA (Rivas and Eddy 2001) and RNAz

(Washietl, Hofacker et al. 2005). These programs rely on pairwise or multiple sequence

alignments from related species. A total of 245 candidates were predicted by QRNA while

RNAz predicted 47. We then designed short oligoprobes against 31 top scoring candidates

8

and checked their expression in 18 different physiological conditions by northern blot

analysis. Unfortunately, despite repeated attempts and excellent positive control signals from

two snoRNAs of different sizes (Fig 2), we have not been able to confirm any of the 31 top

scoring predictions. The poor prediction power of the aforementioned computational

methods was recently substantiated, especially pointing out their high false positive rates

recall (Babak, Blencowe et al. 2007). Our expectation in the beginning of the project was to

identify small ncRNAs such as the microRNAs that were abundant in higher eukaryotes. It

turned out that yeast doesn’t have the repertoire of enzymes to carry out RNA interference.

A B

snR10 (245 nt)

snR18 (102 nt)

snR10

1 2 3 4 5 6 1 2 3 4 5 6

Figure 2. Northern analysis to confirm computationally-predicted ncRNA gene. Northern blots probed with A) oligoprobe against a top scoring QRNA candidate B)

oligoprobe against a top scoring RNAz candidate. Both cases showed negative results. Lanes 1-6: Synthetic defined medium, salt, ethidium bromide, paraquat, caffeine and cadmium chloride. The blots were hybridized first with oligoprobes against the candidates then re-probed with the positive controls (snR10 and/or snR18). Excellent positive control signals were seen in all test blots.

3. YEAST RIBOSOMES

MITOCHONDRIAL RIBOSOME (MITORIBOSOME)

Mitochondria are known to be the power houses of most eukaryotic cells, responsible for

energy production. They are also known to play a central role in ion homeostasis, and

apoptosis (Westermann and Neupert 2003; Chan 2006). In yeast, mitochondria are

considered to be redundant in the respiratory sense under anaerobic conditions due to the

absence of oxygen as the final electron acceptor. Nonetheless, mitochondria could also

assume other roles in anaerobic yeast cell physiology such as in ergosterol biosynthesis,

synthesis and desaturation of fatty acids and membrane lipids, and in general physiological

adaptation to stresses caused by ethanol, toxic oxygen radicals and high sugar concentrations

(O'Connor-Cox, Lodolo et al. 1996).

9

Yeast mitochondria, like all eukaryotic mitochondria, contain their own translational

machinery for the expression of its few genes. Nearly all the constituents of the mitoribosome

are distinct from the cytoplasmic counterpart. The yeast mitoribosome contains two rRNA

molecules, the 15S and 21S (Attardi and Schatz 1988) and estimated to have at least 90

mitochondrial ribosomal proteins (Graack and Wittmann-Liebold 1998). In paper III, 67 non-

essential mitochondrial ribosomal proteins were included in the phenotypic screen.

The mitochondrial rRNA, unlike its cytoplasmic counterpart does not contain a large number

of chemically-modified nucleotides or nucleosides. There are only three modified nucleotides,

a pseudouridine and two 2´-O-methylated riboses, which are carried out wholly by separate

enzymes (Sirum-Connolly and Mason 1993; Ansmant, Massenet et al. 2000; Pintard, Bujnicki

et al. 2002), unlike the modifying enzymes in cytoplasmic rRNA which depends on small

nucleolar RNA (snoRNA) for proper targeting (see discussion below).

CYTOPLASMIC RIBOSOME

The cytoplasmic ribosome is made up of two asymmetric protein-RNA complexes of unequal

sedimentation coefficients: the small and the large ribosomal sub-units. The 60S large sub-

unit (LSU) contains the 25S (28S in higher eukaryotes), 5.8S and 5S rRNAs, while the 40S small

sub-unit (SSU) harbors the 18S rRNA. At present, a total of 79 ribosomal proteins (r-proteins),

33 in the SSU and 46 in the LSU, are considered to be more or less regular residents of the

yeast ribosome, per the criterion of non-dissociation in vitro of these proteins from purified

ribosome preparations at 0.5 M KCl (Kruiswijk and Planta 1974).

To date, there is no high-resolution crystal structure reported yet for any eukaryotic

ribosome. However, the high-conservation in the primary sequence level of ribosomal

components, both for rRNA and many r-proteins from different organisms, gives ample

confidence that the general structural outline of the ribosome may be inferred and extended

across species. This allowed for the modeling of the yeast 80S ribosome, partly based on high-

resolution crystal structures of subunits from the thermophilic bacterium Thermus

thermophilus (Schluenzen, Tocilj et al. 2000; Wimberly, Brodersen et al. 2000) and the

archaebacterium Haloarcula marismortui (Ban, Nissen et al. 2000).

Of the 79 r-proteins, only 44 could be represented in the 3D reconstruction based on

individual homology models of r-protein components and analogous rRNAs assembled into an

10

11.7 Å cryo-EM map of the 80S yeast ribosome. The remaining unaccounted 35 r-proteins are

presumed to dwell in the 19 unresolved clusters of protein densities in the cryo-EM map (Fig

3A) (Spahn, Beckmann et al. 2001; Spahn, Gomez-Lorenzo et al. 2004).

Figure 3. Three dimensional model of yeast 80S cytoplasmic ribosome. A) Forty-four yeast r-protein homology models and analogous rRNAs from H. marismortui large subunit (LSU) and T. thermophilus small subunit (SSU) were fitted into an 11.7 Å cryo-EM map of the yeast 80S ribosome (Spahn, Beckmann et al. 2001; Spahn, Gomez-Lorenzo et al. 2004). (Low resolution surfaces, r-proteins; Chain traces: SSU rRNA, magenta; LSU rRNA, cyan). B) The yeast rRNA modification map indicating modifications considered in paper II. Green dots are pseudouridylations (34 sites) and red dots are 2’-O-ribose methylations (41 sites), corresponding to modification sites targeted by 25 box H/ACA snoRNAs and 34 box C/D snoRNAs, respectively. Many snoRNAs have more than one target site of modification. Some modified residues in yeast cannot be mapped due to lack of correspondence between yeast rRNAs and the the H. marismortui or T. thermophilus rRNAs. Assignments of modified residues based on the work of (Piekna-Przybylska, Decatur et al. 2008). All visual manipulation and figures of the yeast ribosome and its components in this thesis were generated with PDB files (1s1h, 1s1i, 2j00, and 1ffk) downloaded from the Protein Data Bank (Berman, Westbrook et al. 2000) , using the molecular modeling system UCSF Chimera production version 1 build 2470 (Pettersen, Goddard et al. 2004).

CYTOPLASMIC RRNA AND THE SNORNAS

Modified nucleotides are ubiquitous in the cytoplasmic ribosomal RNA (Fig 3B). Their location

are mostly evolutionarily conserved and are predominant in functionally-important regions of

the ribosome (Decatur and Fournier 2002). The two major types of rRNA modifications are 2’-

O-ribose methylation (Nm) and isomerization of uridine to pseudouridine () (Bachellerie,

Cavaille et al. 2002). The number of modifications in rRNA increases with increasing

evolutionary complexity; Escherichia coli has ten s and four Nms, Saccharomyces cerevisiae

has 46 s and 54 Nms, while human rRNA contains more than 90 s and 100 Nms (Ofengand

and Bakin 1997). While modifications in prokaryotic rRNAs are solely carried out by individual

11

protein enzymes, in eukaryotes the site-specificity of the modifications is facilitated by small

nucleolar RNAs (snoRNA) guiding the modifying enzymes to their nucleotide targets via a

base-pairing mechanism. Modification guide snoRNAs may belong in either two classes of

snoRNAs as defined by sequence and structural motifs. Box C/D snoRNAs contain one or two

pairs of small sequence elements called C and D and C’ and D’, while box H/ACA snoRNAs

contain sequence elements called H and ACA. These motifs are required for processing of

snoRNA precursors, and in the case of C/D boxes are also important in localization to the

nucleolus (Decatur and Fournier 2003). Box C/D snoRNAs bind fibrillarin (Nop1p) and direct it

to the sites of methylation in rRNA, while box H/ACA snoRNAs direct the binding of dyskerin

(Cbf5p) to the site of pseudouridylation (Kiss 2002). The protein-snoRNA complex is called

snoRNP (small nucleolar ribonucleoprotein).

It must be noted that there are cytoplasmic rRNA sites that are modified via snoRNP-

independent manner. For example the lone pseudouridylation in 5S rRNA depends entirely on

the activity of the multi-substrate synthase, Pus7p (Decatur and Schnare 2008), while in 25S

rRNA the 2´-O-ribose methylation at G2922 is carried out by Spb1p (Lapeyre and

Purushothaman 2004).

There are in total 76 box C/D and box H/ACA snoRNA genes annotated in the yeast snoRNA

database (Piekna-Przybylska, Decatur et al. 2007; SGD 2008). Genetic analyses yielded only

four lethal phenotypes, i.e. deletion mutants of MRP, U3a/b, snR30 and U14 are inviable,

however, these are all required in pre-rRNA cleavage (Hughes, Konings et al. 1987; Bally,

Hughes et al. 1988; Zagorski, Tollervey et al. 1988; Schmitt and Clayton 1993). One snoRNA

mutant exhibited a clear fitness defect; the slow growing and cold-sensitive

pseudouridylation guide snR10 deletion strain (Tollervey and Guthrie 1985) which later on

was also shown to be involved in pre-rRNA cleavage (Tollervey 1987). For the remaining

snoRNAs no strong link to cellular fitness has been reported despite several attempts (Parker,

Simmons et al. 1988; Lowe and Eddy 1999; Qu, Henras et al. 1999). However, competition

assays and the use of inhibitors directly targeting the ribosomal function have revealed subtle

phenotypes for some knockouts for pseudouridylation yeast snoRNA. Five yeast mutants

lacking a set of box H/ACA snoRNAs guiding pseudouridylations in the peptidyl transferase

center were found to show marginal but significant fitness defects (Badis, Fromont-Racine et

al. 2003; King, Liu et al. 2003). In addition, competition assays revealed subtle defects for

strains lacking two highly conserved pseudouridine modifications guided by snR191 (Badis,

12

Fromont-Racine et al. 2003). As for the loss of 2'-O-ribose methylation with no reported

impact on pre-RNA processing, no fitness defect has so far been reported. Remarkably

however, deleting snR57, which is the box C/D snoRNA guiding the 2’-O-ribose methylation at

position Gm1570 in the 18S rRNA, can actually suppress the lethal phenotype of the deletion

of Nep1p, a protein that is involved in maturation of 18S rRNA (Buchhaupt, Meyer et al.

2006).

4. RIBOSOME AND PROTEIN SYNTHESIS

It has been demonstrated early on that each subunit of the ribosome performs specific

aspects of protein synthesis. For instance, the actual decoding of the mRNA was found to

occur in the small sub-unit (Okamoto and Takanami 1963), while incorporation of amino acid

residues and peptide bond formation transpire in the large sub-unit (Gilbert 1963; Monro,

Cerna et al. 1968). In this level of discourse, the ribosome in itself has almost become

synonymous to translation. In truth however, protein synthesis, especially the eukaryotic one,

is a very complex process which involves the concerted action of many protein factors

interacting transiently with the ribosome.

Translation whether in pro- or eukaryotic system, occurs in four stages: initiation, elongation,

termination and ribosome recycling. Each step requires a number of polypeptides, many of

which associate together forming multi-subunit factors (Kapp and Lorsch 2004; Liljas 2004)

(Fig 4).

The availability of high-resolution ribosomal structures and rich biochemical data allowed for

the detailed description of the molecular events in each stage of translation. This also

permitted the elucidation of roles of some ribosomal proteins in protein synthesis as will be

discussed in the next section.

13

Figure 4. Schematic of the four stages of translation. Each of the four stages: Initiation, Elongation, Termination and Recycling, is characterized by participation of protein complexes. E, P, and A are the exit, peptidyl tRNA, and amino-acyl tRNA sites respectively, in the large subunit (60S) of the ribosome. Modified from (Liljas 2004) to reflect the yeast system. Copyright permission from the author and the publisher (World Scientific).

5. CYTOPLASMIC RIBOSOMAL PROTEINS (CRPS)

GENERAL PROPERTIES

Kruiswijk and Planta (1974) started the isolation and characterisation of cRPs in yeast by two-

dimensional electrophoresis, naming them according to their positions in the gel, as was

originally done earlier with those in E. coli (Kaltschmidt and Wittmann 1970). However it was

not until the publication of the complete sequence of S. cerevisiae genome that the exact

number of genes encoding the cRPs was determined. Using three main criteria: 1) degree of

homology with rp genes from other eukaryotes, 2) high codon adaptation index, and 3)

promoter structure, Planta and Mager (1998) came up with the definitive compilation of cRP

genes in yeast. Assuming functional equivalence between the duplicated cRP genes (59 pairs),

they reported that the yeast genome contains 32 SSU and 46 LSU cRP gene families encoded

by a total of 137 genes (Planta and Mager 1998).

The following year, a powerful approach that made direct use of mass spectrophotometry

identified a novel component of the yeast and human 40S subunit (Link, Eng et al. 1999). This

mammalian protein RACK1, Asc1p in yeast, was finally mapped in the SSU (Gerbasi, Weaver et

14

al. 2004; Sengupta, Nilsson et al. 2004) and is now considered a core ribosomal protein,

bringing the total number of yeast cRPs to 79.

A typical characteristic of virtually all cRP genes, which they share with other highly expressed

genes such as glycolytic enzymes, is the ability of their promoter regions to bind the multi-

functional transcription factor Rap1p in vivo (Lieb, Liu et al. 2001). Indeed, it was shown

bioinformatically that majority of cRP gene promoters have tandem Rap1p-binding sites or in

places where this is absent, Abf1p or Reb1p binding sites are present (Lascaris, Mager et al.

1999). But as mentioned, these transcription factors are not exclusive to cRP genes. Rap1 for

instance, is known to perform many functions such as regulation of many glycolytic enzymes,

silencing of mating-type genes, maintenance of telomere ends (Shore 1994) and even

controlling the osmotic stress-responsive gene, GPD1 (Eriksson, Alipour et al. 2000). Only very

recently was a cRP gene-specific transcription factor, Fhl1p, found (Lee, Rinaldi et al. 2002).

This protein regulates cRP gene expression through TOR via the highly conserved PKA

signaling pathway, a well-studied signal transduction pathway controlling cell growth in

response to diverse environmental cues (Martin, Soulard et al. 2004). Regulatory elements,

such as the Fhl1p binding-site on cRP gene promoters, are expected to modulate the

coordinated transcription of cRP genes resulting in global attenuation of their mRNAs during

stressed condition, such as rapamycin, heat shock treatment, nutrient depletion, and other

environmental insults (Gasch, Spellman et al. 2000). The fact that not all cRP genes bind Fhl1p

in vivo (two immunoprecipitation studies both identified only around 55% of all cRP genes

that were strongly bound by Fhl1p (Schawalder, Kabani et al. 2004; Wade, Hall et al. 2004)

point to other stress-specific regulatory mechanisms which might govern cRP genes.

Another feature of the cRP genes is the high frequency of introns among them. Of the 250

genes containing introns in the yeast genome, 99 are cRP genes (Planta and Mager 1998).

Although the biological significance of selection for introns in many cRP genes is not

understood, splicing of cRP mRNAs definitely adds another level of regulation in this class of

genes (Warner 1989).

59 of the 79 of cRPs have paralogs which resulted from an ancient whole genome duplication

event (Kellis, Birren et al. 2004). Nonetheless, only a handful of these paralog pairs retained

100% sequence identity in the amino acid level. Some diverged significantly even in the

nucleotide level that the codon adaptation index (CAI) differs considerably between paralog

15

pairs, as in the case of RPL22A and RPL22B which have 0.86 and 0.29 CAI, respectively (Planta

and Mager 1998). A high CAI indicates high gene expression (Akashi and Eyre-Walker 1998).

Of the 19 singletons, 15 are lethal when deleted, while only 5 of the 59 paralog pairs are

essential. However, using conditional gene constructs, double deletion of both paralogs of 26

of 28 small subunit proteins resulted in lethality, which could be rescued by overexpression of

any one of the two alleles (Ferreira-Cerca, Poll et al. 2005).

Ribosomal proteins are generally short, ranging from only 51 to 387 amino acid residues long.

The crystal structures of many cRPs reveal a two-domain architecture consisting of globular

and tailed regions (Fig 5). The tail parts, which might occur internally or in the termini regions

of r-proteins, are highly-unordered and very flexible, mainly due to the presence of large

amount of small residues such as glycine. This is one of the reasons why many cRPs

containing tailed extensions or those completely lacking globular domains have been very

difficult, if not impossible, to be structurally determined in isolation (Brodersen and Nissen

2005).

Chemically, cRPs are markedly enriched with Lys and Arg giving them a very alkaline

character, with the exception of the four acidic r-proteins of the ribosomal stalk in the LSU.

There is also a very low amount of negatively charged residues such as Glu and Asp (Planta

and Mager 1998). This is comparable only to histones which bind and stabilise chromatin

structures. The highly alkaline property of majority of r-proteins, particularly in their tail

regions, is essential in counteracting the negative charges of the phosphate group of the rRNA

backbone leading to its correct conformation during ribosome assembly (Wilson and Nierhaus

2005). Noteworthy as well is the high prevalence of small, hydrophobic amino acids such as

Ala, Val and Gly (Lin, Kuang et al. 2002), relating to the observation that many other highly-

expressed genes tend to contain such small amino acid residues (Jansen and Gerstein 2000).

16

Figure 5. Globular and tail domains of r-proteins. The globular regions are characterized by ordered secondary structures while the tail parts consist of flexible loops which extend into the ribosome making extensive contacts with the rRNA.

EVOLUTIONARY CONSERVATION

A systematic comparison of cRP genes from 66 complete genomes representing Bacteria (45

species), Archaea (14 species) and Eukarya (7 species) not only revealed a high degree of

conservation for many of the cRP gene families but also the presence of kingdom-specific cRP

genes (Lecompte, Ripp et al. 2002) (Fig 6). It is readily noticeable that the number of r-

proteins also increases as one goes up the phylogenetic lineage, hinting that the protein

components are recent additions to the ancestral ribosome of the postulated ancient RNA

world (Wool 1996).

One interesting observation is the lack of r-proteins exclusively shared by Bacteria with either

Archaea or Eukarya, in agreement with the theory that the last common ancestor of Archaea

and Eukarya diverged from the Bacteria before their own separation occurred. Not only are

many r-proteins kingdom-specific, but differences within each kingdom are also present. For

example, within the eukaryotes, the mammalian L28 gene also present in the nematode

(Caenorhabditis elegans), fruitfly (Drosophila melanogaster), and plant (Arabidopsis thaliana)

has no homolog in the budding yeast. In higher plants, an additional P-protein called P3,

17

complexes to P1/P2 which are the usual acidic phosphoproteins forming the lateral stalk

structure of the large subunits of animals, fungi and protozoans (Szick, Springer et al. 1998).

Figure 6. Intersection of r-proteins in the three kingdoms of life. Only 34 r-proteins are universally conserved. The bacterial kingdom does not exclusively share any r-protein homologies with either the Archaea (BA:0) or the Eukarya (BE:0).The total number of r-proteins in eukarya increased by one with the recent addition of Asc1p in the small sub-unit. Modified from Lecompte et.al. (2002).

There are 34 universally-conserved cRPs, 15 in the SSU and 19 in the LSU (Table 1). Many of

these have been shown to be involved in ribosome assembly in E. coli (Held, Ballou et al.

1974; Rohl and Nierhaus 1982) and virtually all were implicated in the maturation and

transport of pre-18S rRNA in yeast (Ferreira-Cerca, Poll et al. 2005). A few act as inter-subunit

bridges (E. coli numbering: S13, S15, S19, L2, L5, and L14) while three (L22, L29, L24) surround

the exit tunnel of the large subunit (Yusupov, Yusupova et al. 2001). In E. coli, S9, S13, S17,

L1, L11, L15, L24, L29 and L30, none of which has a duplicate, are among the conserved r-

proteins which are non-essential (Dabbs 1986; Herr, Nelson et al. 2001). In yeast, 70% of the

conserved r-proteins have duplicates, confounding the interpretation of the result of

homozygous single-deletions of these r-proteins showing viability (Winzeler, Shoemaker et al.

1999). Nonetheless, complete deletion of paralog r-proteins in yeast generally result in

lethality (Ferreira-Cerca, Poll et al. 2005).

BAE: 34

B:23

AE: 33 E:12 A:1

BA:0 BE:0

Bacteria: 57

Archaea: 68

Eukarya: 79

18

Table 1. The universally conserved ribosomal proteins in the three kingdoms. Modified from Wilson and Nierhaus (2005). Primary data source: http://www.expasy.org/cgi-bin/lists?ribosomp.txt (22-Jul-2008 release)

Small subunit Large subunit

Bacteria Archaea Low

Eukarya High

Eukarya Bacteria Archaea

Low Eukarya

High Eukarya

E. coli H.

marismortui Yeast Rat E. coli H.

marismortui Yeast Rat

S2 S2 S0 Sa L1 L1 L1 L10a

S3 S3 S3 S3 L2 L2 L2 L8

S4 S4 S9 S9 L3 L3 L3 L3

S5 S5 S4 S2 L4 L4 L4 L4

S7 S7 S5 S5 L5 L5 L11 L11

S8 S8 S22 S15a L6 L6 L9 L9

S9 S9 S16 S16 L10 L10 P0(A0) P0

S10 S10 S20 S20 L11 L11 L12 L12

S11 S11 S14 S14 L12 L12 P1/P2 P1/P2

S12 S12 S23 S23 L13 L13 L16 L13a

S13 S13 S18 S18 L14 L14 L23 L23

S14 S14 S29 S29 L15 L15 L28 L27a

S15 S15 S13 S13 * L7Ae L8 L7a

S17 S17 S11 S11 L18 L18 L5 L5

S19 S19 S15 S15 L22 L22 L17 L17

L23 L23 L25 L23a

L24 L24 L26 L26

L29 L29 L35 L35

L30 L30 L7 L7

*Ortholog absent in E.coli but present in Bacillus subtilis and many Staphylococcal species as L7Ae-like putative ribosomal protein genes.

ROLES IN TRANSLATION

With the availability of atomic resolution ribosome structures, investigating the role of

individual ribosomal protein components has become more and more mechanistic in the

context of actual events in protein synthesis. Coupled with chemical probing techniques to

study rRNA conformations, targeted mutagenesis, biochemical and genetic approaches,

various studies have successfully assigned specific roles during translation for many ribosomal

proteins.

In prokaryotes, the roles of r-proteins in distinct steps of translation are well-documented

(Brodersen and Nissen 2005; Wilson and Nierhaus 2005). Extensive studies on E. coli r-

proteins revealed their diverse functions from recognition and decoding of translational

substrates (mRNAs and tRNAs), to conferring resistance against antibiotics which inhibit

translation. A number of r-proteins were also localized in functionally important sites such as

19

surrounding the PTC (L2 and L3) or lining the exit tunnel (L4, L22, L23 and L24). Many of these

prokaryotic r-proteins are universally conserved and have been modeled in the corresponding

sites of the yeast ribosome. It is therefore possible that their functions might be conserved as

well. Table 2 lists some yeast ribosomal proteins implicated in specific steps during

translation.

Table 2. Yeast ribosomal proteins involved in specific steps during translation.

The heart of the translational process, that is peptide formation in the PTC, occurs within the

exclusive domain of the ribosomal RNA. However, four tailed-proteins have extensions

protruding close to this region (Fig 7), which could influence the optimal positioning of the

tRNA-bound amino acid substrates undergoing peptidyl transfer.

RPL10 and RPL3 are both singletons and are lethal when deleted in yeast. RPL2 and RPL4 both

have duplicates which are non-essential, and are therefore amenable to our phenotypic

profiling approach. In paper III, we found that the deletion mutants of RPL2B and RPL4A

showed fairly similar phenotypic profiles, specifically exhibiting sensitivity towards tert-butyl

hydroperoxide, placing both of them in the oxidative sub-cluster. This is a rare instance where

we found ribosomal proteins in close proximity to each other, both in the spatial position in

the three dimensional structure and in the context of phenotypic clustering of their

corresponding deletion mutants. In general, we did not observe any correlation between the

spatial positioning of the cRPs in the ribosome, and the phenotypic clustering of the

corresponding gene deletion mutants.

Ribosomal protein

Function during protein synthesis Reference

rpL3 Allosteric switch in the A-site coordinating binding of

elongation factors (Meskauskas and Dinman 2007)

rpS0 Involved in the recruitment of the translation initiation

factor eIF3 to the small subunit (Valasek, Mathew et al. 2003)

rpS9 Accurate decoding of mRNA (Vincent and Liebman 1992; Alksne, Anthony et al. 1993)

rpS2 Control of translational accuracy (Alksne, Anthony et al. 1993; Synetos, Frantziou et al. 1996)

rpL39 Translational accuracy (Dresios, Derkatch et al. 2000)

rpL5 Anchoring of peptidyl-tRNA to P-site (Meskauskas and Dinman 2001)

rpL41 Translocation of t-RNAs (Dresios, Panopoulos et al. 2003).

rpL10 Subunit joining (Eisinger, Dick et al. 1997)

20

Figure 7. Some r-proteins reach into the peptidyl transferase centre via their tails. Although the PTC is completely surrounded by rRNA helices (red ribbon), four r-proteins can reach the vicinity via their tail extensions. The anisomycin antibiotic which binds in the PTC is shown for reference.

ROLES IN RIBOSOME BIOGENESIS

Making a functional ribosome is a huge task requiring the cooperative functioning of more

than 170 pre-ribosomal factors in the post-transcriptional processing of rRNAs which involves

splicing, modification, folding, transport and assembly (Fatica and Tollervey 2002). Despite

the abundance of these trans-acting factors, the r-proteins themselves have also been shown

to play specific roles in the maturation, assembly and transport of the rRNAs. An example is

rpS14, whose tail extension was shown to be necessary for maturation of 43S pre-ribosomes

(Jakovljevic, de Mayolo et al. 2004). Another is rpS15 required for exit of the 40S subunit

precursors (Leger-Silvestre, Milkereit et al. 2004).

A systematic approach was used to address the specific roles of yeast small subunit proteins

in pre-18S rRNA processing, localization and export (Ferreira-Cerca, Poll et al. 2005). Using

strains whose r-proteins are under the strict control of the GAL1 promoter, the authors were

able to monitor the effect of specific r-protein depletion in the distinct steps of 18S rRNA

processing pathway. The conclusion was that, except for rpS7, rpS30 and rpS31, virtually all of

21

the SSU r-proteins play a role in the maturation and transport of 18S rRNA precursors. In a

recent study, an LSU r-protein, Rpl3p, was also shown to be involved in early assembly of 60S

ribosomal subunit and in the subsequent steps of their maturation and export (Rosado,

Kressler et al. 2007). In paper IV, polysomal analyses of knockouts of 20 ribosomal protein

genes of the small subunit and 16 ribosomal protein genes of the large subunit in basal

conditions confirmed the importance of individual ribosomal proteins in the final assembly of

functional ribosomes.

ROLES IN OTHER CELLULAR PROCESSES

A decade ago, the extra-ribosomal functions of some r-proteins from various organisms were

compiled (Wool 1996). The diversity of enumerated functions, ranging from control of viral

replication in E. coli (Yancey and Matson 1991) to developmental regulation in higher

eukaryotes (Fisher, Beer-Romero et al. 1990; Watson, Konrad et al. 1992) was seen as

evidence “that ribosomal proteins might have been co-opted for other cellular functions” and

that they were not originally designed specifically for the ribosome. It was an intriguing

pronouncement in support of the widely-held view regarding the evolutionary history of

ribosomal proteins, i.e. they came later into the ancient ribosome prototype exclusively made

up of RNA. This view only strengthens by the day as new bi-functional r-proteins working

outside the periphery of the translational apparatus are found:

i) The human L13A which is released from the 60S subunit into the cytoplasm upon

phosphorylation in response to in vivo levels of interferon-γ. In the cytoplasm, L13A

specifically binds a structural element at the 3’-end of the ceruplasmin mRNA,

effectively silencing its translation (Mazumder, Sampath et al. 2003).

ii) The mammalian ribosomal protein RACK1, yeast Asc1p, serves as a scaffold among

diverse kinases and receptors ultimately linking signals from various transduction

pathways (McCahill, Warwicker et al. 2002). Interestingly, it has also been observed to

exist as ribosome-bound and unbound forms in S. cerevisiae (Baum, Bittins et al.

2004), humans (Ceci, Gaviraghi et al. 2003) and fission yeast (Shor, Calaycay et al.

2003).

iii) Another novel function being recently attributed to ribosomal proteins, specifically

those of the 40S small sub-unit, is their possible role in translational control via

recruitment of specific mRNAs. Termed as the “ribosome filter” hypothesis, it posits

22

that components of the ribosomal subunit may act as regulatory elements, acting as

filters, which may either enhance or inhibit the translation of various mRNAs via

differential binding interactions (Mauro and Edelman 2002). Selective translation

occurs depending on the affinity of a class of mRNAs to the rRNA or to ribosomal

proteins.

iv) In Schizosaccharomyces pombe, RPL32-2 was shown to act as a potential

transcriptional regulator in the nucleus (Jing, Sheng et al. 2006).

v) A number of ribosomal protein genes have been found to have increased gene

expression in mammalian tumor cell lines. Human RPS3a specifically has been shown

to fulfill an important role in cell transformation and apoptosis (Naora 1999).

Interestingly, in zebra fish, at least 11 cRP genes were found to be haplo-insufficient

tumor suppressors (Amsterdam, Sadler et al. 2004).

vi) Increased replicative life span (RLS) was found in yeast with deletion in 60S ribosomal

protein genes (Steffen, MacKay et al. 2008). In another study, strains deleted for small

subunit ribosomal protein genes were also shown to have increased RLS (Chiocchetti,

Zhou et al. 2007).

Taking some of the above instances of r-proteins possibly working off the translational

apparatus, it is not difficult to imagine the ribosome serving also as a repository of

functionally important proteins (Mazumder, Sampath et al. 2003). In fact, the exchange of r-

proteins on and off the ribosome has been known long ago for the acidic r-proteins in yeast

that forms the ribosomal stalk in the large sub-unit (Zinker and Warner 1976). The amount of

ribosome-bound forms is somehow correlated with the metabolic activity of the cells, being

substantially reduced during the stationary phase of growth (Saenz-Robles, Remacha et al.

1990). Also, Qsr1p (RPL10) which was found to be essential in subunit joining is also

exchangeable, cycling on and off large subunits in the cytoplasm (Dick, Eisinger et al. 1997).

23

THESIS SUMMARY Discovery consists in seeing what everybody else has seen,

and thinking what nobody else has thought. Albert Szerg-Györgyi

6. DIVERSE CELLULAR ROLES OF SNORNA GENES

The “notoriety” of snoRNA genes as “zero phenotype genes” in genetic depletion analysis in

the wild type background is widely-known in the RNA field. As mentioned in an earlier

section, only when the growth assays of some snoRNA gene knockouts were shifted from

traditional phenotyping methodology on solid media to more sophisticated competition

assays that a handful was shown to have fitness contributions to cellular growth.

Our high-resolution phenomics methodology confirmed in both paper I and paper II the

generally marginal nature of phenotypic response of snoRNA gene deletion knockouts in

basal conditions. Indeed, in our phenotypic screen in paper II we found only 4 out of 65

snoRNA gene deletion mutants showing strong growth rate defects in the basal medium.

Nonetheless, we were able to capture a plethora of phenotypes in many growth conditions

providing evidence on their potential roles in cellular stress response. Specifically we

uncovered an apparent link of snoRNA genes to respiratory functions (see 9. FUNCTIONAL

SPECIALIZATION IN YEAST RIBOSOMES).

The biochemical role of snoRNAs as guides in the site-specific modifications in the rRNA has

long been known. However, the fact that snoRNAs had to make contacts with the rRNAs

during the modification process forming transient snoRNA-rRNA duplexes leaves the question

whether the snoRNA has some chaperone activity during the folding process of the rRNA.

Indeed, it has been shown using computer simulation that archaeal box C/D snoRNAs could

provide chaperone effects for the proper folding of rRNA by restricting folding domains

(Schoemaker and Gultyaev 2006). This confounds the interpretation of genetic depletion

experiments involving snoRNA genes. There’s always the lingering question whether the

observed phenotype displayed by a snoRNA gene knockout mutant is due to the failure of

modification or due to the lack of snoRNA molecule itself which might function in some other

biochemical context. This can probably be resolved by looking at the phenotypes of snoRNA

gene knockouts, whose otherwise present snoRNA gene products target the same

modification site. We were able to partly address this question in paper II where we observed

24

that snr39and snr59showed dissimilar phenotypic profiles. SNR39 and SNR59 are intron-

encoded box C/D snoRNA genes contained in RPL7A and RPL7B respectively (Ghazal, Ge et al.

2005). RPL7A and RPL7B are nearly-identical paralogous cRP genes and the intronic snoRNAs

they harbor both guide the 2’-O-ribose methylation at position A807 in the 25S rRNA. We saw

from paper II that while snr39 displayed extreme sensitivity to the alternative carbon

sources thus belonging to the “non-respiratory” sub cluster, snr59did not show sensitivity

to any particular growth inhibitor at all. The fact that residue A807 is modified (via snR59)

even when SNR39 has been deleted excludes the lack of 2’-O-ribose methylation as the cause

of phenotypic defect seen in snr39This result implies that snR39 could be involved in a yet

unknown fitness-contributing cellular process.

7. RIBOSOMAL PROTEIN GENES AS “TURBO” GENES

The “turbo” growth phenomenon was used to describe the importance of genes for maximal

growth in optimal conditions while losing their importance during growth-perturbing

conditions. In an earlier genome-wide phenotypic screen this phenomenon was noted in

many of the core cellular components, such as general transcription factors and regulators of

central metabolic processes (Warringer 2003).

Our analysis of phenotypic data in paper III indicated that turbo growth phenomenon was a

widespread feature among cRPs and mRPs, with approximately 66% of the 110 cRPs and 43%

of the 67 mRPs to have reduced importance in many stress conditions. These ribosomal

proteins seemed to be required only when there is unperturbed proliferation during optimal

conditions when the translational machinery is working at maximal capacity. However, when

cell growth is slowed-down by various external stresses the translational rate might no longer

be limiting for growth.

8. PARALOGOUS CRP GENES: “SAME, SAME BUT DIFFERENT”

A large proportion of cytoplasmic ribosomal protein genes have paralogs. Our phenotypic

study in paper III contained 110 cRP deletion knockouts, and 94 of them have paralogous

associations. Many of these duplicated cRPs are very well-conserved in fact, 11 pairs in this

study were identical. Assuming that the biochemical function of ribosomal proteins is

confined within the perimeters of translation, it was very surprising that in general, we found

paralogous pairs to be phenotypically-divergent. Our findings were in fact corroborated by

25

earlier studies focused on specific paralogous pairs. For instance, in the RPS9A and RPS9B

pair, the omnipotent suppression phenotype was shown only for RPS9B (Vincent and Liebman

1992). RPS9B was also found to have higher transcription levels than RPS9A (Vincent and

Liebman 1992; Mager, Planta et al. 1997; Pnueli and Arava 2007). Another example is the

deletion of RPL7A which moderately impairs growth and affects budding while deletion of

RPL7B has no effect on growth (Mizuta, Hashimoto et al. 1995). In studies on yeast replicative

life span (RLS), it was often the case that only one of the paralogs contribute to increased RLS

(Chiocchetti, Zhou et al. 2007; Steffen, MacKay et al. 2008).

In paper III, analysis of evolutionary histories of the cRP paralogs based on the studies of

Kellis et al (Kellis, Birren et al. 2004) allowed us to relate the “ancestral” and “derived”

paralogs variants to their differential importance in growth conditions. Whereas we found the

evolutionary most conserved cRP paralogs (ancestral) to be of higher importance during

growth in optimal conditions, the derived cRP paralog variants appeared to be selected and

optimized for functionality during various environmental challenges.

9. FUNCTIONAL SPECIALIZATION IN YEAST RIBOSOMES

We systematically established functional links among the ribosomal proteins and the

cytoplasmic rRNA modifications in yeast ribosomes. Integrating the phenotypic data of

snoRNA (from paper II), cytoplasmic and mitochondrial ribosomal protein (from paper III)

deletion mutants, we observed distinct functional features delineating specific phenotypic

sub clusters (Table 3 and Fig 8).

THE “NON-RESPIRATORY” SUB CLUSTERS

Remarkably, the chemogenetic landscape was heavily dominated by the extreme response of

select groups of deletion mutants to non-fermentable or alternative carbon sources (Fig 8). It

was striking that this characteristic phenotypic response could be resolved further into three

levels according to the magnitudes of sensitivity toward galactose, raffinose, maltose and

ethanol. These sugars were able to set clear demarcation lines between particular groups of

cRP, mRP and snoRNA gene knockouts.

26

Table 3. Number of knockouts in each sub cluster according to type of gene deletion. Some phenotypic sub clusters contain almost exclusively only a single type of gene deletion mutant, such as the anisomycin sub cluster while some contain mix of all three such as the oxidative stress sub cluster.

Phenotypic sub cluster snoRNA cRP mRP

Low response 7 10 1

Mitochondrial non-respiratory 0 1 51

snoRNA non-respiratory 27 0 1

Cytoplasmic non-respiratory 6 25 5

Rapamycin cluster 8 23 0

Heat shock 1 26 3

Oxidative stress 6 25 6

Anisomycin 10 0 0

TOTAL 65 110 67

For instance, the “mitochondrial non-respiratory” sub-cluster did not grow in all the

alternative carbon sources and was dominated almost exclusively by mitochondrial ribosomal

proteins (mRPs) (except for a single cRP gene deletion mutant, rpl1b) (Table 3). The next

level of non-respiratory sensitivity was those not growing only in maltose and ethanol, and

moderate growth defects in galactose and raffinose. Except for mrpl27this was comprised

of snoRNA deletion mutants, hence “snoRNA non-respiratory”. The last level of alternative

carbon source sensitivity was the “cytoplasmic non-respiratory” which only exhibited slow

growth in maltose and ethanol, while growing normally on galactose and raffinose. This sub-

cluster is interesting as it is the only non-respiratory sub-cluster that contains a considerable

mix of all three snoRNA, cRP and mRP deletion mutants.

The extreme sensitivity of majority of the mRP gene knockouts to alternative carbon sources

was not a surprise since mRPs are required in the production of important respiratory chain

components encoded in the mitochondrial genome. As for the sensitivity of a very tight group

of snoRNA deletion mutants to alternative carbon sources, we confirmed in paper II via

respirometry that it is due to defective respiratory capacities. Although it could be linked to

negative influence on mitochondrial functions, a more complex physiological explanation is

warranted since they were also the only deletion mutants to exhibit hyper resistance towards

anisomycin, for which no other mutants belonging to the other two non-respiratory sub

clusters showed resistance to.

27

Figure 8. Hierarchical clustering of all profiled deletion mutants from papers II and III. Hierarchical clustering of gene-by-environments interactions between 110 cRP, 67 mRP and 65 snoRNA gene deletion mutants in 19 stress environments. Colors indicate increased (red) or reduced (green) stress tolerance. Complete names of environments may be found in paper II and III separately for snoRNAs and r-protein gene deletion strains, respectively.

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OTHER STRESS-SPECIFIC SUB CLUSTERS

It is noticeable that some sub clusters include only specific classes of gene deletion mutants.

For example, the “anisomycin” sub cluster comprised only of snoRNA gene knockouts.

Anisomycin is a macrolide that competitively inhibit peptide bond formation by blocking the

hydrophobic crevice of the peptidyl transferase center from accepting incoming amino acid

residues bound to amino-acyl tRNA (Hansen, Moore et al. 2003).

The “rapamycin” sub cluster was comprised only of cRP and snoRNA knockouts. This is

consistent with the fact that there is a degree of coordination between the biosynthesis of

snoRNA and components of the cytoplasmic translational machinery (Bachellerie, Cavaille et

al. 2000). Eight snoRNA genes are intronic to cRP genes, and many monocistronic snoRNA

genes are also found adjacent to cRP genes implying co-transcriptional regulation. Indeed

canonical cRP gene promoter-binding sites such as Rap1, Abf1p and Fhl1p are also found

upstream in a number of snoRNA gene loci (Qu, Henras et al. 1999; Harbison, Gordon et al.

2004). Since Fhl1p is controlled via the TOR pathway which is inhibited by rapamycin, the

sensitivities observed in both cRP and snoRNA gene deletion mutants could be a direct

consequence of the antibiotic inhibition.

GENERAL TRANSLATIONAL FEATURES OF MUTANTS HARBORING STRESS-SPECIFIC CLASSES OF

CYTOPLASMIC RIBOSOMAL PROTEINS

For the identified stress-specific clusters discussed above, we investigated in paper IV some

general translational features of a substantial number of deletion mutants by velocity

sedimentation in sucrose gradient. Such studies have been done before in specific rp gene

knockouts however, to our knowledge this is the first systematic polysome profiling analyses

on a substantial number of rp gene knockouts of the same genetic background. We confirmed

the observed importance of individual ribosomal proteins in the final assembly of a functional

ribosome. While we observed the general translational inhibition in the growth of wild type

as a consequence of ethanol or rapamycin-induced stress, we obtained surprising results

regarding the behavior of the cRP gene mutants grown in the same stress conditions.

Specifically, we identified cRP gene deletions strains which were resistant to such translation

inhibitory effects of stress.

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10. LINKING PHENOTYPES TO RIBOSOMAL STRUCTURE

We attempted, in paper I, to relate the phenotypic responses of 20 C/D snoRNA gene

deletion mutants to the location of their cognate 2’-O-ribose methylation targets in the

cytoplasmic rRNA. Specifically we spatially mapped the locations of the modifications into

high resolution structural models of ribosomes to see whether the clustering among the

phenotypes of C/D snoRNA gene deletion knockouts in ribosome-specific antibiotics, will

correlate with their spatial positioning in the ribosome. However, no correlation has been

found. New phenotypic data from paper II also did not show any specific phenotype-spatial

location correlations, both in the level of ribosome-specific phenotypes, i.e. anisomycin-

sensitives (Fig 9A) and environment-wide level phenotypic clustering, i.e. “respiratory” vs

“non respiratory” clusters (Fig 9B).

The lack of correlation between the tight phenotypic responses of the box C/D snoRNA

deletions and the spatial location of the corresponding modified residues probably reflects

the highly complicated nature of allosteric physical interactions in the ribosome, i.e. local

structural change in rRNA can trigger significant conformational changes in distant parts of

the ribosomal structure (Bashan, Zarivach et al. 2003).

A B

Figure 9. Spatial mapping of modified rRNA modifications on the ribosome. A) The target modification sites of snoRNAs whose gene deletion knockouts exhibited sensitivity to anisomycin were mapped into the rRNA. The snoRNA knockouts belong to the tightly clustered “Anisomycin sub cluster” in Fig 8. Anisomycin inhibits translation by blocking the hydrophobic crevice in the peptidyl transferase center (Hansen, Moore et al. 2003). The scattered distribution of modification sites point to complex allosteric interactions affecting antibiotic binding. B) No spatial correlation was observed in the level of environment-wide phenotypic clustering for snoRNA gene knockouts (LSU: large subunit, SSU: small subunit).

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31

PERSPECTIVES If at first the idea is not absurd, then there is no hope for it.

Albert Einstein

11. SNORNAS IN CELLULAR STRESS RESPONSE

The diversity of phenotypic responses, ranging from extreme sensitivity to extreme resistance

in various stresses, we uncovered for snoRNA gene deletion knockouts in paper I and II

indicated a hitherto unknown functional importance of snoRNA molecules in cellular stress

response. The link to cellular respiration is particularly intriguing and deserves further

attention. Functional networks connecting snoRNAs to such cellular processes would be

expected to be complex. Indeed the potentially multiple fitness contributions of snoRNA

molecules to the cell, first as chaperones during rRNA maturation and secondly via the rRNA

modifications they caused to exist, might confound the problem. Nevertheless results from

paper II indicated functional separation between the snoRNA molecule itself and the rRNA

modifications they cause, as illustrated by two snoRNAs having the same target modification

site yet the two mutants lacking the snoRNAs showed highly divergent phenotypes. Whatever

the case, there are a number of techniques currently available that should be able to help

resolve the intricacies of snoRNA functions as will be described below.

SYNTHETIC GENE INTERACTION OF SNORNA GENES

The landmark publication of Tong et al. which made use of a large-scale systematic

construction of double deletion mutants referred to as synthetic genetic array (SGA) analysis

allowed for the identification of comprehensive functional genetic networks for a number of

genes with roles in cytoskeletal organization, DNA synthesis or repair, and two genes with

uncharacterized functions (Tong, Evangelista et al. 2001). In the current SGA set up, a query

strain carrying a deletion or mutation of the gene of interest is crossed against an array of

approximately 5000 viable protein-coding gene deletion mutants. The resulting meiotic

progeny can then be scored for fitness defect. Newer SGA analysis variants patterned from

the original synthetic lethal screens include genetic suppression, plasmid shuffling, and

dosage lethality (Tong and Boone 2006). The power of this brute force approach lies in the

systematic identification of functional relationships between genes leading to global mapping

32

of gene function. Unfortunately, non-coding RNA genes have so far been neglected in such

SGA screens.

In paper II, we constructed 65 snoRNA gene knockouts in the same genetic background as the

rest of the Saccharomyces Deletion Project collection. This gives us a great opportunity to

perform synthetic lethal screens among these snoRNA gene deletion mutants and versus the

whole protein-coding gene knockout collection. Future SGA analysis of other genes will also

be more complete upon the inclusion of the snoRNA gene knockouts. The idea seems

promising since one study already found a synthetic suppression effect of the deletion of

SNR57 on the lethal phenotype of NEP1 gene which encodes a protein involved in 18S rRNA

maturation (Buchhaupt, Meyer et al. 2006).

With the currently available variants of SGA analysis, a more global view of snoRNA gene

function is within reach.

DIFFERENTIAL RIBOSOMAL RNA MODIFICATION

The identification of resistant phenotypes in a number of stress conditions upon deletion of

specific snoRNAs led us to postulate that the modification of sites in the ribosomal RNA may

be differentially-regulated, i.e. environment-dependent rRNA modification patterns. Not so

much is known about the transcriptional regulation of snoRNA genes except that they are

thought to be co-regulated with the components of the translational apparatus (Qu, Henras

et al. 1999; Bachellerie, Cavaille et al. 2000). In a large-scale identification of cis-regulatory

sequences within the yeast genome involving 203 known DNA-binding transcriptional

regulators, a map of transcriptional regulatory code was produced (Harbison, Gordon et al.

2004). Genome-wide location analysis was used to pinpoint the genomic occupancy for each

of the transcription factors, essentially mapping the target promoter regions for all the genes.

We mined their data to determine which transcriptional regulators were associated with

snoRNA genes. Remarkably, not only the known ribosomal protein transcription factors, such

as Rap1p, Fhl1p, and Sfp1p were identified to be present in a number of snoRNA genes, we

also found transcription factors involved in stress response to be bound to some snoRNA

genes. Some specific examples are Adr1p, which is a carbon-source responsive transcription

factor known to be required for the transcription of genes needed for transcription of the

glucose-repressed gene ADH2, of peroxisomal protein genes, and of genes required for

ethanol, glycerol, and fatty acid utilization. Gcn4p, a transcriptional activator of amino acid

33

biosynthetic genes in response to amino acid starvation, was also found to bind some snoRNA

genes. Interestingly, Skn7p required for optimal induction of heat-shock genes in response to

oxidative stress was also found to bind a snoRNA gene. All in all, a total of 21 transcription

factors, a number of which are known to be activated during stress conditions, were shown to

bind to promoter sequences of some of the snoRNA genes. This can be taken as an evidence

of environment-specific expression of snoRNA genes, and hence differential rRNA

modifications. Such a scenario is consistent with the prevailing consensus hypothesis

regarding rRNA modifications as “fine-tuners” of the translational apparatus (Decatur and

Fournier 2002). Indeed it is during stressful environments when such fine-tuning activity

would be expected to be of great utility.

To be able to test the environment-dependent modification patterns of rRNAs, mapping the

location of close to a hundred of modified residues would be a challenging task, especially

when the most commonly used procedures in mapping pseudouridylation and 2´-O-ribose

methylation are reverse transcription-based approaches involving radioactive-labeling

procedures and expensive reagents (Maden, Corbett et al. 1995; Maden 2001). Recently, the

novel use of RNA-cleaving deoxyribozymes (DNAzymes) were used to detect 2´-O-ribose

methylations and pseudouridylations in the yeast rRNA (Buchhaupt, Peifer et al. 2007). The

method was fast and cost-effective, requiring only DNA oligos designed to specifically cleave

at desired target sites. We tried to utilize this method and tested on a number of our snoRNA

deletion constructs (Fig 10). We designed two DNAzymes directed against a modification site

in the 25S rRNA and another in the 18S rRNA. The procedure was indeed straightforward

requiring minimum amount of reagents (only reaction buffers and low concentration

polyacrylamide gels). However the fact that the cleavage efficiency never reaches 100% could

be a cause of concern when trying to detect differential modification of rRNA. For instance,

the hypothesized presence of heterogeneous rRNA populations in some environmental

condition, wherein some are modified while some are not, would be difficult to resolve if the

DNAzyme-catalyzed cleavage reaction has low cleavage efficiency. A possible solution would

be to determine the exact cleavage efficiencies of DNAzymes by using standard amounts of

RNA substrates. However, to ensure pure preparation of RNA i.e. without modification sites,

one might need to do separate in vitro synthesis of RNA molecules.

34

A B

Figure 10. Use of DNAzymes to detect lack of 2’-O-ribose methylations in the rRNA. DNAzymes are RNA-cleaving DNA molecules identified by in vitro selection methods. The cleavage reaction requires the 2’-hydroxyl group. Thus methylated rRNA nucleoside prevents cleavage. In the wild type, the target nucleoside is methylated while in snoRNA deletion mutants, the target nucleosides retain the 2’-hydroxyl group allowing the DNAzyme-catalyzed cleavage of RNA to proceed. In A) snR61 guides the 2’-O-methylation at A1133 in the 25S rRNA and in B) snR53 guides the 2’-O-methylation at A796 in the 18S rRNA. The expected cleavage products are shown in both cases when the snoRNAs were deleted. Note that in both cases, 100% cleavage of 25S or 18S was not reached. Materials and methods were based on (Buchhaupt, Peifer et al. 2007).

12. THE “RIBOSOME CODE”?

With the observation that paralogous ribosomal proteins have different specialized functions,

Komilii et al., drew a parallel comparison between transcriptional regulation, known as the

“histone code” and translational regulation, which they proposed to call the “ribosome code”

(Komili, Farny et al. 2007). The “histone code”, which is the canonical model for

transcriptional regulation, posits that the transcriptional state of a given region of chromatin

is determined by distinct combinations of histone proteins, their post-translational

modifications, and DNA modifications (Jenuwein and Allis 2001). In the same manner, the

heterogeneity of the ribosome, and thus its various translational states, could be realized by

the different combinations of paralogous ribosomal proteins, their post-translational

modifications, and possible differential rRNA modifications. The tantalizing prospect of

finding demethylases that could reverse methylated nucleoside or nucleotide in the rRNA

would be an exciting discovery just like when the first histone demethylase was found just a

couple of years ago (Shi, Lan et al. 2004) after the dogmatic status of histone methylations as

being static and enzymatically irreversible since the 1970s.

Our finding about the existence of stress-specific sub clusters of cytoplasmic ribosomal

protein and snoRNA gene deletion mutants supports the idea of compositionally distinct

ribosomes which are customized according to environmental cues. That or, the direct

participation of many ribosomal protein components and snoRNAs in cellular stress response

are two possible explanations of the rich phenotypic diversity we uncovered in this study.

35

13. TOWARDS A SYSTEM-WIDE ANALYSIS OF THE TRANSLATIONAL APPARATUS

We have come a long way in understanding the inner workings of the ribosome since its

discovery almost 50 years ago. From its first description by George Palade as a small

particulate component of the cytoplasm (Palade 1955), we can now literally see through the

ribosome and explain what makes it tick. The simplicity of the mono-functional protein

synthesis machinery whose parts are well-kept within the perimeters of translational burden

is indeed a marvelous conception and perhaps best kept that way. However, evidence

regarding the bi-functionality of many ribosomal proteins, and their apparent involvement in

other cellular processes continues to pile up.

In this age of innovative high-throughput bioassays, it is imperative that we investigate the

functional links of ribosomal components to other aspects of cellular physiology. The many

variants of synthetic gene array (SGA) analysis such as the synthetic lethal screens, genetic

suppression, and dosage lethality (Tong and Boone 2006) applied to the whole genome

including the non-coding RNA genes, using all the non-essential ribosomal proteins and

snoRNA genes as “query” genes would provide a more holistic understanding of their

functions that is not limited within the confines of translation.

36

37

ACKNOWLEDGMENTS To serve humanity alone is the sole purpose of life.

Leo Tolstoy

As this is perhaps the only page of the thesis comprehensible to any reader (or the only part people actually read), my intention was to write one kickass acknowledgment. One which will melt your heart, bring you to tears and leave the most profound impression on your soul. But as I am a man of repressed emotions (at least when I’m sober; fortunately I am at this very moment) I will skip the drama and go straight to the point without the obligatory aggrandizing descriptions of persons without whom this obra wouldn’t have seen the light of day. Besides, you already know how great you all are! That might sound boring but I really want to avoid sounding like I’m leaving this world for good, and like, you know will not see you anymore. *sniff*

So without much further ado, my sincerest gratitude to THE man, Anders Blomberg.

Special thanks to Jonas Warringer. Luciano Fernandez-Ricaud. Magnus Alm Rosenblad. Tore Samuelsson. And of course, Paul Piccinelli.

Our collaborators in Toronto, Corey Nislow and co-workers – snoRNA work.

To Per Sunnerhagen – polysome. Eva Albers and Christer Larsson – respirometer.

To all the current and former Blombergians: Ellinor, Susanna, Lasse, Mikael, Beidong, Robert, Elke, Amir and Johan.

To all the current and former Lundbergians, and Lundberglings. I’m not really sure what they mean but you may choose which category you want to belong to. But both refers to people who have walked (and still walking in) the hallowed corridors of Lundberg Lab. You guys are the epitome of knowledge and patience. I heart you all.

To the people in the admin, most especially to Agneta.

To the wonderful people of the Research School in Genomics and Bioinformatics. And the Research School itself for the funding.

To Ernest, Rickard, David, Daniel P, Henrik, Douglas and Daniel D.

To the Filipino support group in Göteborg: The Manalili, Esporlas, Limeta and Caligagan Families. To the Yagitz of Sweden: Topey, Ian, Blue, Red, and Teng. And Mary Chris. And past Pinoy Chalmerists. To the Lunden basketball team.

To the Skåne support group: Sara and Krille. And to Åsa’s circle of friends, maffian and others.

To my parents and all my siblings and their loved ones in the Philippines and somewhere else.

To Jan and Christl.

And to Åsa. And Elias, our very own babylycka.

I take a humble bow and, thank you all from the bottom of my heart.

And as the Swedes say, “Vi ses!”

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39

REFERENCES There are no facts, only interpretations.

Friedrich Nietzsche

Akashi, H. and A. Eyre-Walker (1998). "Translational selection and molecular evolution." Curr Opin Genet Dev 8(6): 688-93.

Alksne, L. E., R. A. Anthony, et al. (1993). "An accuracy center in the ribosome conserved over 2 billion years." Proc Natl Acad Sci U S A 90(20): 9538-41.

Amsterdam, A., K. C. Sadler, et al. (2004). "Many ribosomal protein genes are cancer genes in zebrafish." PLoS Biol 2(5): E139.

Ansmant, I., S. Massenet, et al. (2000). "Identification of the Saccharomyces cerevisiae RNA:pseudouridine synthase responsible for formation of psi(2819) in 21S mitochondrial ribosomal RNA." Nucleic Acids Res 28(9): 1941-6.

Attardi, G. and G. Schatz (1988). "Biogenesis of mitochondria." Annu Rev Cell Biol 4: 289-333. Babak, T., B. J. Blencowe, et al. (2007). "Considerations in the identification of functional RNA

structural elements in genomic alignments." BMC Bioinformatics 8: 33. Bachellerie, J. P., J. Cavaille, et al. (2002). "The expanding snoRNA world." Biochimie 84(8):

775-90. Bachellerie, J. P., J. Cavaille, et al. (2000). Nucleotides modifications of eukaryotic rRNAs: the

world of small nucleolar RNA guides revisited. The Ribosome: Structure, Function Antibiotics and Cellular Interactions. R. A. Garret, S. R. Douthwaite, A. Liljaset al. Washington, DC, ASM Press: 191-204.

Badis, G., M. Fromont-Racine, et al. (2003). "A snoRNA that guides the two most conserved pseudouridine modifications within rRNA confers a growth advantage in yeast." Rna 9(7): 771-9.

Bally, M., J. Hughes, et al. (1988). "SnR30: a new, essential small nuclear RNA from Saccharomyces cerevisiae." Nucleic Acids Res 16(12): 5291-303.

Ban, N., P. Nissen, et al. (2000). "The complete atomic structure of the large ribosomal subunit at 2.4 A resolution." Science 289(5481): 905-20.

Barnett, J. A. (1998). "A history of research on yeasts. 1: Work by chemists and biologists 1789-1850." Yeast 14(16): 1439-51.

Bashan, A., R. Zarivach, et al. (2003). Ribosomal crystallography: peptide bond formation and its inhibition. Biopolymers. 70: 19-41.

Baum, S., M. Bittins, et al. (2004). "Asc1p, a WD40-domain containing adaptor protein, is required for the interaction of the RNA-binding protein Scp160p with polysomes." Biochem J 380(Pt 3): 823-30.

Berman, H. M., J. Westbrook, et al. (2000). "The Protein Data Bank." Nucleic Acids Res 28(1): 235-42.

Brodersen, D. E. and P. Nissen (2005). "The social life of ribosomal proteins." Febs J 272(9): 2098-108.

Buchhaupt, M., B. Meyer, et al. (2006). "Genetic evidence for 18S rRNA binding and an Rps19p assembly function of yeast nucleolar protein Nep1p." Mol Genet Genomics 276(3): 273-84.

Buchhaupt, M., C. Peifer, et al. (2007). "Analysis of 2'-O-methylated nucleosides and pseudouridines in ribosomal RNAs using DNAzymes." Anal Biochem 361(1): 102-8.

Ceci, M., C. Gaviraghi, et al. (2003). "Release of eIF6 (p27BBP) from the 60S subunit allows 80S ribosome assembly." Nature 426(6966): 579-84.

40

Chan, D. C. (2006). "Mitochondria: dynamic organelles in disease, aging, and development." Cell 125(7): 1241-52.

Chang, M., M. Bellaoui, et al. (2002). "A genome-wide screen for methyl methanesulfonate-sensitive mutants reveals genes required for S phase progression in the presence of DNA damage." Proc Natl Acad Sci U S A 99(26): 16934-9.

Chiocchetti, A., J. Zhou, et al. (2007). "Ribosomal proteins Rpl10 and Rps6 are potent regulators of yeast replicative life span." Exp Gerontol 42(4): 275-86.

Choesmel, V., S. Fribourg, et al. (2008). "Mutation of ribosomal protein RPS24 in Diamond-Blackfan anemia results in a ribosome biogenesis disorder." Hum Mol Genet 17(9): 1253-63.

Cmejla, R., J. Cmejlova, et al. (2007). "Ribosomal protein S17 gene (RPS17) is mutated in Diamond-Blackfan anemia." Hum Mutat 28(12): 1178-82.

Dabbs, E. (1986). Mutant studies on the prokaryotic ribosome. Structure, function and genetics of ribosomes. B. Hardesty and G. Kramer. New York ; Berlin, Springer-Vlg: 733-748.

Decatur, W. A. and M. J. Fournier (2002). "rRNA modifications and ribosome function." Trends Biochem Sci 27(7): 344-51.

Decatur, W. A. and M. J. Fournier (2003). "RNA-guided nucleotide modification of ribosomal and other RNAs." J Biol Chem 278(2): 695-8.

Decatur, W. A. and M. N. Schnare (2008). "Different mechanisms for pseudouridine formation in yeast 5S and 5.8S rRNAs." Mol Cell Biol 28(10): 3089-100.

Desmoucelles, C., B. Pinson, et al. (2002). "Screening the yeast "disruptome" for mutants affecting resistance to the immunosuppressive drug, mycophenolic acid." J Biol Chem 277(30): 27036-44.

Dick, F. A., D. P. Eisinger, et al. (1997). "Exchangeability of Qsr1p, a large ribosomal subunit protein required for subunit joining, suggests a novel translational regulatory mechanism." FEBS Lett 419(1): 1-3.

Dresios, J., I. L. Derkatch, et al. (2000). "Yeast ribosomal protein L24 affects the kinetics of protein synthesis and ribosomal protein L39 improves translational accuracy, while mutants lacking both remain viable." Biochemistry 39(24): 7236-44.

Dresios, J., P. Panopoulos, et al. (2003). "A dispensable yeast ribosomal protein optimizes peptidyltransferase activity and affects translocation." J Biol Chem 278(5): 3314-22.

Ebert, B. L., J. Pretz, et al. (2008). "Identification of RPS14 as a 5q- syndrome gene by RNA interference screen." Nature 451(7176): 335-9.

Eisinger, D. P., F. A. Dick, et al. (1997). "Qsr1p, a 60S ribosomal subunit protein, is required for joining of 40S and 60S subunits." Mol Cell Biol 17(9): 5136-45.

Ericson, E. (2006). High-resolution phenomics to decode yeast stress physiology. Department of Cell and Molecular Biology. Göteborg, Göteborg University.

Eriksson, P., H. Alipour, et al. (2000). "Rap1p-binding sites in the saccharomyces cerevisiae GPD1 promoter are involved in its response to NaCl." J Biol Chem 275(38): 29368-76.

Fatica, A. and D. Tollervey (2002). "Making ribosomes." Curr Opin Cell Biol 14(3): 313-8. Fernandez-Ricaud, L., J. Warringer, et al. (2007). "PROPHECY--a yeast phenome database,

update 2006." Nucleic Acids Res 35(Database issue): D463-7. Ferreira-Cerca, S., G. Poll, et al. (2005). "Roles of eukaryotic ribosomal proteins in maturation

and transport of pre-18S rRNA and ribosome function." Mol Cell 20(2): 263-75. Fisher, E. M., P. Beer-Romero, et al. (1990). "Homologous ribosomal protein genes on the

human X and Y chromosomes: escape from X inactivation and possible implications for Turner syndrome." Cell 63(6): 1205-18.

41

Gasch, A. P., P. T. Spellman, et al. (2000). "Genomic expression programs in the response of yeast cells to environmental changes." Mol Biol Cell 11(12): 4241-57.

Gavin, A. C., M. Bosche, et al. (2002). "Functional organization of the yeast proteome by systematic analysis of protein complexes." Nature 415(6868): 141-7.

Gerbasi, V. R., C. M. Weaver, et al. (2004). "Yeast Asc1p and mammalian RACK1 are functionally orthologous core 40S ribosomal proteins that repress gene expression." Mol Cell Biol 24(18): 8276-87.

Ghaemmaghami, S., W. K. Huh, et al. (2003). "Global analysis of protein expression in yeast." Nature 425(6959): 737-41.

Ghazal, G., D. Ge, et al. (2005). "Genome-wide prediction and analysis of yeast RNase III-dependent snoRNA processing signals." Mol Cell Biol 25(8): 2981-94.

Giaever, G., A. M. Chu, et al. (2002). "Functional profiling of the Saccharomyces cerevisiae genome." Nature 418(6896): 387-91.

Gilbert, W. (1963). "Polypeptide synthesis in Escherichia coli. I. Ribosomes and the active complex." J Mol Biol 6: 374-88.

Goffeau, A., B. G. Barrell, et al. (1996). "Life with 6000 genes." Science 274(5287): 546, 563-7. Graack, H. R. and B. Wittmann-Liebold (1998). "Mitochondrial ribosomal proteins (MRPs) of

yeast." Biochem J 329 ( Pt 3): 433-48. Hansen, J. L., P. B. Moore, et al. (2003). "Structures of five antibiotics bound at the peptidyl

transferase center of the large ribosomal subunit." J Mol Biol 330(5): 1061-75. Harbison, C. T., D. B. Gordon, et al. (2004). "Transcriptional regulatory code of a eukaryotic

genome." Nature 431(7004): 99-104. Heiss, N. S., S. W. Knight, et al. (1998). "X-linked dyskeratosis congenita is caused by

mutations in a highly conserved gene with putative nucleolar functions." Nat Genet 19(1): 32-8.

Held, W. A., B. Ballou, et al. (1974). "Assembly mapping of 30 S ribosomal proteins from Escherichia coli. Further studies." J Biol Chem 249(10): 3103-11.

Herr, A. J., C. C. Nelson, et al. (2001). "Analysis of the roles of tRNA structure, ribosomal protein L9, and the bacteriophage T4 gene 60 bypassing signals during ribosome slippage on mRNA." J Mol Biol 309(5): 1029-48.

Hughes, J. M., D. A. Konings, et al. (1987). "The yeast homologue of U3 snRNA." Embo J 6(7): 2145-55.

Huh, W. K., J. V. Falvo, et al. (2003). "Global analysis of protein localization in budding yeast." Nature 425(6959): 686-91.

Huxley, T. H. (1893). Yeast: The Contemporary Review (1871). Collected Essays Discourses: Biological & Geological. London, MacMillan and Co. VIII: 112-138.

Jakovljevic, J., P. A. de Mayolo, et al. (2004). "The carboxy-terminal extension of yeast ribosomal protein S14 is necessary for maturation of 43S preribosomes." Mol Cell 14(3): 331-42.

Jansen, R. and M. Gerstein (2000). "Analysis of the yeast transcriptome with structural and functional categories: characterizing highly expressed proteins." Nucleic Acids Res 28(6): 1481-8.

Jenuwein, T. and C. D. Allis (2001). "Translating the histone code." Science 293(5532): 1074-80.

Jing, W., Y. Sheng, et al. (2006). "The ribosomal protein L32-2 (RPL32-2) of S. pombe exhibits a novel extraribosomal function by acting as a potential transcriptional regulator." FEBS Lett 580(7): 1827-32.

42

Kaltschmidt, E. and H. G. Wittmann (1970). "Ribosomal proteins. XII. Number of proteins in small and large ribosomal subunits of Escherichia coli as determined by two-dimensional gel electrophoresis." Proc Natl Acad Sci U S A 67(3): 1276-82.

Kapp, L. D. and J. R. Lorsch (2004). "The molecular mechanics of eukaryotic translation." Annu Rev Biochem 73: 657-704.

Kellis, M., B. W. Birren, et al. (2004). "Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae." Nature 428(6983): 617-24.

King, T. H., B. Liu, et al. (2003). "Ribosome structure and activity are altered in cells lacking snoRNPs that form pseudouridines in the peptidyl transferase center." Mol Cell 11(2): 425-35.

Kiss, T. (2002). "Small nucleolar RNAs: an abundant group of noncoding RNAs with diverse cellular functions." Cell 109(2): 145-8.

Komili, S., N. G. Farny, et al. (2007). "Functional specificity among ribosomal proteins regulates gene expression." Cell 131(3): 557-71.

Kruiswijk, T. and R. J. Planta (1974). "Analysis of the protein composition of yeast ribosomal subunits by two-dimensional polyacrylamide gel electrophoresis." Mol Biol Rep 1(7): 409-15.

Lapeyre, B. and S. K. Purushothaman (2004). "Spb1p-directed formation of Gm2922 in the ribosome catalytic center occurs at a late processing stage." Mol Cell 16(4): 663-9.

Lascaris, R. F., W. H. Mager, et al. (1999). "DNA-binding requirements of the yeast protein Rap1p as selected in silico from ribosomal protein gene promoter sequences." Bioinformatics 15(4): 267-77.

Lashkari, D. A., J. L. DeRisi, et al. (1997). "Yeast microarrays for genome wide parallel genetic and gene expression analysis." Proc Natl Acad Sci U S A 94(24): 13057-62.

Lecompte, O., R. Ripp, et al. (2002). "Comparative analysis of ribosomal proteins in complete genomes: an example of reductive evolution at the domain scale." Nucleic Acids Res 30(24): 5382-90.

Lee, T. I., N. J. Rinaldi, et al. (2002). "Transcriptional regulatory networks in Saccharomyces cerevisiae." Science 298(5594): 799-804.

Leeuwenhoek, A. (1680). Antoni van Leeuwenhoek, dated at Delft, to Thomas Gale. Early Letters L1 to The Royal Society. London.

Leger-Silvestre, I., P. Milkereit, et al. (2004). "The ribosomal protein Rps15p is required for nuclear exit of the 40S subunit precursors in yeast." Embo J 23(12): 2336-47.

Lieb, J. D., X. Liu, et al. (2001). "Promoter-specific binding of Rap1 revealed by genome-wide maps of protein-DNA association." Nat Genet 28(4): 327-34.

Liljas, A. (2004). Structural aspects of protein synthesis. Singapore ; Hackensack, N.J., World Scientific Pub. Co.

Lin, K., Y. Kuang, et al. (2002). "Conserved codon composition of ribosomal protein coding genes in Escherichia coli, Mycobacterium tuberculosis and Saccharomyces cerevisiae: lessons from supervised machine learning in functional genomics." Nucleic Acids Res 30(11): 2599-607.

Link, A. J., J. Eng, et al. (1999). "Direct analysis of protein complexes using mass spectrometry." Nat Biotechnol 17(7): 676-82.

Lowe, T. M. and S. R. Eddy (1999). "A computational screen for methylation guide snoRNAs in yeast." Science 283(5405): 1168-71.

Maden, B. E. (2001). "Mapping 2'-O-methyl groups in ribosomal RNA." Methods 25(3): 374-82.

43

Maden, B. E., M. E. Corbett, et al. (1995). "Classical and novel approaches to the detection and localization of the numerous modified nucleotides in eukaryotic ribosomal RNA." Biochimie 77(1-2): 22-9.

Martens, J. A., L. Laprade, et al. (2004). "Intergenic transcription is required to repress the Saccharomyces cerevisiae SER3 gene." Nature 429(6991): 571-4.

Martin, D. E., A. Soulard, et al. (2004). "TOR regulates ribosomal protein gene expression via PKA and the Forkhead transcription factor FHL1." Cell 119(7): 969-79.

Mauro, V. P. and G. M. Edelman (2002). "The ribosome filter hypothesis." Proc Natl Acad Sci U S A 99(19): 12031-6.

Mazumder, B., P. Sampath, et al. (2003). "Regulated release of L13a from the 60S ribosomal subunit as a mechanism of transcript-specific translational control." Cell 115(2): 187-98.

McCahill, A., J. Warwicker, et al. (2002). "The RACK1 scaffold protein: a dynamic cog in cell response mechanisms." Mol Pharmacol 62(6): 1261-73.

McCutcheon, J. P. and S. R. Eddy (2003). "Computational identification of non-coding RNAs in Saccharomyces cerevisiae by comparative genomics." Nucleic Acids Res 31(14): 4119-28.

Meskauskas, A. and J. D. Dinman (2001). "Ribosomal protein L5 helps anchor peptidyl-tRNA to the P-site in Saccharomyces cerevisiae." Rna 7(8): 1084-96.

Meskauskas, A. and J. D. Dinman (2007). "Ribosomal protein L3: gatekeeper to the A site." Mol Cell 25(6): 877-88.

Monro, R. E., J. Cerna, et al. (1968). "Ribosome-catalyzed peptidyl transfer: substrate specificity at the P-site." Proc Natl Acad Sci U S A 61(3): 1042-9.

Morimoto, K., S. Lin, et al. (2007). "The functions of RPS19 and their relationship to Diamond-Blackfan anemia: a review." Mol Genet Metab 90(4): 358-62.

Mustacchi, R., S. Hohmann, et al. (2006). "Yeast systems biology to unravel the network of life." Yeast 23(3): 227-38.

Naora, H. (1999). "Involvement of ribosomal proteins in regulating cell growth and apoptosis: translational modulation or recruitment for extraribosomal activity?" Immunol Cell Biol 77(3): 197-205.

O'Connor-Cox, E., E. Lodolo, et al. (1996). "Mitochondrial relevance to yeast fermentative performance: a review." Journal of the Institute of Brewing 102: 19-25.

Ofengand, J. and A. Bakin (1997). "Mapping to nucleotide resolution of pseudouridine residues in large subunit ribosomal RNAs from representative eukaryotes, prokaryotes, archaebacteria, mitochondria and chloroplasts." J Mol Biol 266(2): 246-68.

Okamoto, T. and M. Takanami (1963). "Interaction of Ribosomes and Natural Polyribonucleotides." Biochim Biophys Acta 76: 266-74.

Olivas, W. M., D. Muhlrad, et al. (1997). "Analysis of the yeast genome: identification of new non-coding and small ORF-containing RNAs." Nucleic Acids Res 25(22): 4619-25.

Oliver, S. G., Q. J. van der Aart, et al. (1992). "The complete DNA sequence of yeast chromosome III." Nature 357(6373): 38-46.

Osterberg, M., H. Kim, et al. (2006). "Phenotypic effects of membrane protein overexpression in Saccharomyces cerevisiae." Proc Natl Acad Sci U S A 103(30): 11148-53.

Outeiro, T. F. and F. Giorgini (2006). "Yeast as a drug discovery platform in Huntington's and Parkinson's diseases." Biotechnol J 1(3): 258-69.

Palade, G. E. (1955). "A small particulate component of the cytoplasm." J Biophys Biochem Cytol 1(1): 59-68.

44

Parker, R., T. Simmons, et al. (1988). "Genetic analysis of small nuclear RNAs in Saccharomyces cerevisiae: viable sextuple mutant." Mol Cell Biol 8(8): 3150-9.

Perocchi, F., E. Mancera, et al. (2008). "Systematic screens for human disease genes, from yeast to human and back." Mol Biosyst 4(1): 18-29.

Pettersen, E. F., T. D. Goddard, et al. (2004). "UCSF Chimera--a visualization system for exploratory research and analysis." J Comput Chem 25(13): 1605-12.

Piekna-Przybylska, D., W. A. Decatur, et al. (2007). "New bioinformatic tools for analysis of nucleotide modifications in eukaryotic rRNA." Rna.

Piekna-Przybylska, D., W. A. Decatur, et al. (2008). "The 3D rRNA modification maps database: with interactive tools for ribosome analysis." Nucleic Acids Res 36(Database issue): D178-83.

Pierce, S. E., R. W. Davis, et al. (2007). "Genome-wide analysis of barcoded Saccharomyces cerevisiae gene-deletion mutants in pooled cultures." Nat Protoc 2(11): 2958-74.

Pintard, L., J. M. Bujnicki, et al. (2002). "MRM2 encodes a novel yeast mitochondrial 21S rRNA methyltransferase." EMBO J 21(5): 1139-47.

Planta, R. J. and W. H. Mager (1998). "The list of cytoplasmic ribosomal proteins of Saccharomyces cerevisiae." Yeast 14(5): 471-7.

Qu, L. H., A. Henras, et al. (1999). "Seven novel methylation guide small nucleolar RNAs are processed from a common polycistronic transcript by Rat1p and RNase III in yeast." Mol Cell Biol 19(2): 1144-58.

Rivas, E. and S. R. Eddy (2001). "Noncoding RNA gene detection using comparative sequence analysis." BMC Bioinformatics 2: 8.

Rohl, R. and K. H. Nierhaus (1982). "Assembly map of the large subunit (50S) of Escherichia coli ribosomes." Proc Natl Acad Sci U S A 79(3): 729-33.

Rosado, I. V., D. Kressler, et al. (2007). "Functional analysis of Saccharomyces cerevisiae ribosomal protein Rpl3p in ribosome synthesis." Nucleic Acids Res 35(12): 4203-13.

Ruggero, D., S. Grisendi, et al. (2003). "Dyskeratosis congenita and cancer in mice deficient in ribosomal RNA modification." Science 299(5604): 259-62.

Saenz-Robles, M. T., M. Remacha, et al. (1990). "The acidic ribosomal proteins as regulators of the eukaryotic ribosomal activity." Biochim Biophys Acta 1050(1-3): 51-5.

Schattner, P., W. A. Decatur, et al. (2004). "Genome-wide searching for pseudouridylation guide snoRNAs: analysis of the Saccharomyces cerevisiae genome." Nucleic Acids Res 32(14): 4281-96.

Schawalder, S. B., M. Kabani, et al. (2004). "Growth-regulated recruitment of the essential yeast ribosomal protein gene activator Ifh1." Nature 432(7020): 1058-61.

Scheper, G. C., M. S. van der Knaap, et al. (2007). "Translation matters: protein synthesis defects in inherited disease." Nature Reviews Genetics 8(9): 711-723.

Schluenzen, F., A. Tocilj, et al. (2000). "Structure of functionally activated small ribosomal subunit at 3.3 angstroms resolution." Cell 102(5): 615-23.

Schmitt, M. E. and D. A. Clayton (1993). "Nuclear RNase MRP is required for correct processing of pre-5.8S rRNA in Saccharomyces cerevisiae." Mol Cell Biol 13(12): 7935-41.

Schoemaker, R. J. and A. P. Gultyaev (2006). "Computer simulation of chaperone effects of Archaeal C/D box sRNA binding on rRNA folding." Nucleic Acids Res 34(7): 2015-26.

Schwimmer, C., M. Rak, et al. (2006). "Yeast models of human mitochondrial diseases: from molecular mechanisms to drug screening." Biotechnol J 1(3): 270-81.

Sengupta, J., J. Nilsson, et al. (2004). "Identification of the versatile scaffold protein RACK1 on the eukaryotic ribosome by cryo-EM." Nat Struct Mol Biol 11(10): 957-62.

SGD (2008). "Saccharomyces Genome Database."

45

Shi, Y., F. Lan, et al. (2004). "Histone demethylation mediated by the nuclear amine oxidase homolog LSD1." Cell 119(7): 941-53.

Shor, B., J. Calaycay, et al. (2003). "Cpc2/RACK1 is a ribosome-associated protein that promotes efficient translation in Schizosaccharomyces pombe." J Biol Chem 278(49): 49119-28.

Shore, D. (1994). "RAP1: a protean regulator in yeast." Trends Genet 10(11): 408-12. Sirum-Connolly, K. and T. L. Mason (1993). "Functional requirement of a site-specific ribose

methylation in ribosomal RNA." Science 262(5141): 1886-9. Spahn, C. M., R. Beckmann, et al. (2001). "Structure of the 80S ribosome from Saccharomyces

cerevisiae--tRNA-ribosome and subunit-subunit interactions." Cell 107(3): 373-86. Spahn, C. M., M. G. Gomez-Lorenzo, et al. (2004). "Domain movements of elongation factor

eEF2 and the eukaryotic 80S ribosome facilitate tRNA translocation." Embo J 23(5): 1008-19.

Steffen, K. K., V. L. MacKay, et al. (2008). "Yeast life span extension by depletion of 60s ribosomal subunits is mediated by Gcn4." Cell 133(2): 292-302.

Steinmetz, L. M., C. Scharfe, et al. (2002). "Systematic screen for human disease genes in yeast." Nat Genet 31(4): 400-4.

Synetos, D., C. P. Frantziou, et al. (1996). "Mutations in yeast ribosomal proteins S28 and S4 affect the accuracy of translation and alter the sensitivity of the ribosomes to paromomycin." Biochim Biophys Acta 1309(1-2): 156-66.

Szick, K., M. Springer, et al. (1998). "Evolutionary analyses of the 12-kDa acidic ribosomal P-proteins reveal a distinct protein of higher plant ribosomes." Proc Natl Acad Sci U S A 95(5): 2378-83.

Thatcher, J. W., J. M. Shaw, et al. (1998). "Marginal fitness contributions of nonessential genes in yeast." Proc Natl Acad Sci U S A 95(1): 253-7.

Tollervey, D. (1987). "A yeast small nuclear RNA is required for normal processing of pre-ribosomal RNA." Embo J 6(13): 4169-75.

Tollervey, D. and C. Guthrie (1985). "Deletion of a yeast small nuclear RNA gene impairs growth." Embo J 4(13B): 3873-8.

Tong, A. H. and C. Boone (2006). "Synthetic genetic array analysis in Saccharomyces cerevisiae." Methods Mol Biol 313: 171-92.

Tong, A. H., M. Evangelista, et al. (2001). "Systematic genetic analysis with ordered arrays of yeast deletion mutants." Science 294(5550): 2364-8.

Tong, A. H., G. Lesage, et al. (2004). "Global mapping of the yeast genetic interaction network." Science 303(5659): 808-13.

Valasek, L., A. A. Mathew, et al. (2003). "The yeast eIF3 subunits TIF32/a, NIP1/c, and eIF5 make critical connections with the 40S ribosome in vivo." Genes Dev 17(6): 786-99.

Vincent, A. and S. W. Liebman (1992). "The yeast omnipotent suppressor SUP46 encodes a ribosomal protein which is a functional and structural homolog of the Escherichia coli S4 ram protein." Genetics 132(2): 375-86.

Wade, J. T., D. B. Hall, et al. (2004). "The transcription factor Ifh1 is a key regulator of yeast ribosomal protein genes." Nature 432(7020): 1054-8.

Warner, J. R. (1989). "Synthesis of ribosomes in Saccharomyces cerevisiae." Microbiol Rev 53(2): 256-71.

Warringer, J. (2003). Genetic robustness during environmental stress. Department of Cell and Molecular Biology. Göteborg, Göteborg University.

Warringer, J., D. Anevski, et al. (2008). "Chemogenetic fingerprinting by analysis of cellular growth dynamics." BMC Chem Biol 8: 3.

46

Warringer, J. and A. Blomberg (2003). "Automated screening in environmental arrays allows analysis of quantitative phenotypic profiles in Saccharomyces cerevisiae." Yeast 20(1): 53-67.

Warringer, J., E. Ericson, et al. (2005). Manuscript. Warringer, J., E. Ericson, et al. (2003). "High-resolution yeast phenomics resolves different

physiological features in the saline response." Proc Natl Acad Sci U S A 100(26): 15724-9.

Washietl, S., I. L. Hofacker, et al. (2005). "Fast and reliable prediction of noncoding RNAs." Proc Natl Acad Sci U S A 102(7): 2454-9.

Watson, K. L., K. D. Konrad, et al. (1992). "Drosophila homolog of the human S6 ribosomal protein is required for tumor suppression in the hematopoietic system." Proc Natl Acad Sci U S A 89(23): 11302-6.

Westermann, B. and W. Neupert (2003). "'Omics' of the mitochondrion." Nat Biotechnol 21(3): 239-40.

Wilson, D. N. and K. H. Nierhaus (2005). "Ribosomal proteins in the spotlight." Crit Rev Biochem Mol Biol 40(5): 243-67.

Wimberly, B. T., D. E. Brodersen, et al. (2000). "Structure of the 30S ribosomal subunit." Nature 407(6802): 327-39.

Winzeler, E. A., D. D. Shoemaker, et al. (1999). "Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis." Science 285(5429): 901-6.

Wool, I. G. (1996). "Extraribosomal functions of ribosomal proteins." Trends Biochem Sci 21(5): 164-5.

Yan, Z., M. Costanzo, et al. (2008). "Yeast Barcoders: a chemogenomic application of a universal donor-strain collection carrying bar-code identifiers." Nat Methods 5(8): 719-25.

Yancey, J. E. and S. W. Matson (1991). "The DNA unwinding reaction catalyzed by Rep protein is facilitated by an RHSP-DNA interaction." Nucleic Acids Res 19(14): 3943-51.

Yoon, A., G. Peng, et al. (2006). "Impaired control of IRES-mediated translation in X-linked dyskeratosis congenita." Science 312(5775): 902-6.

Yusupov, M. M., G. Z. Yusupova, et al. (2001). "Crystal structure of the ribosome at 5.5 A resolution." Science 292(5518): 883-96.

Zagorski, J., D. Tollervey, et al. (1988). "Characterization of an SNR gene locus in Saccharomyces cerevisiae that specifies both dispensible and essential small nuclear RNAs." Mol Cell Biol 8(8): 3282-90.

Zinker, S. and J. R. Warner (1976). "The ribosomal proteins of Saccharomyces cerevisiae. Phosphorylated and exchangeable proteins." J Biol Chem 251(6): 1799-807.